Overview

Dataset statistics

Number of variables62
Number of observations10939
Missing cells189525
Missing cells (%)27.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 MiB
Average record size in memory289.3 B

Variable types

CAT35
NUM13
DATE7
BOOL7

Warnings

event_date has constant value "10939" Constant
user_pseudo_id has a high cardinality: 336 distinct values High cardinality
device.mobile_model_name has a high cardinality: 70 distinct values High cardinality
device.mobile_os_hardware_model has a high cardinality: 89 distinct values High cardinality
geo.region has a high cardinality: 70 distinct values High cardinality
geo.city has a high cardinality: 155 distinct values High cardinality
device.vendor_id has a high cardinality: 281 distinct values High cardinality
item_subcategory has a high cardinality: 196 distinct values High cardinality
item_name has a high cardinality: 480 distinct values High cardinality
item_id has a high cardinality: 494 distinct values High cardinality
search_term has a high cardinality: 543 distinct values High cardinality
ga_session_id is highly correlated with ga_session_id.valueHigh correlation
ga_session_id.value is highly correlated with ga_session_idHigh correlation
ga_session_number is highly correlated with ga_session_number.valueHigh correlation
ga_session_number.value is highly correlated with ga_session_numberHigh correlation
firebase_previous_id is highly correlated with firebase_screen_idHigh correlation
firebase_screen_id is highly correlated with firebase_previous_idHigh correlation
device.mobile_brand_name has 242 (2.2%) missing values Missing
device.mobile_model_name has 242 (2.2%) missing values Missing
geo.region has 288 (2.6%) missing values Missing
geo.city has 186 (1.7%) missing values Missing
device.vendor_id has 1013 (9.3%) missing values Missing
engaged_session_event has 294 (2.7%) missing values Missing
session_engaged has 10487 (95.9%) missing values Missing
firebase_screen_id has 453 (4.1%) missing values Missing
firebase_screen_class has 453 (4.1%) missing values Missing
engagement_time_msec has 6232 (57.0%) missing values Missing
entrances has 10466 (95.7%) missing values Missing
freeride has 10727 (98.1%) missing values Missing
firebase_previous_id has 7310 (66.8%) missing values Missing
firebase_previous_class has 7310 (66.8%) missing values Missing
item_subcategory has 10035 (91.7%) missing values Missing
recommended has 9965 (91.1%) missing values Missing
item_category has 9988 (91.3%) missing values Missing
item_name has 9965 (91.1%) missing values Missing
item_number has 9965 (91.1%) missing values Missing
item_id has 9965 (91.1%) missing values Missing
debug_event has 10697 (97.8%) missing values Missing
previous_os_version has 10882 (99.5%) missing values Missing
search_term has 10137 (92.7%) missing values Missing
search_book has 10137 (92.7%) missing values Missing
search_type has 10137 (92.7%) missing values Missing
search_subcategory has 10880 (99.5%) missing values Missing
search_category has 10880 (99.5%) missing values Missing
event_server_timestamp_offset is highly skewed (γ1 = 21.54516149) Skewed
ga_session_id.value is highly skewed (γ1 = -90.20279982) Skewed
ga_session_id is highly skewed (γ1 = -90.20279982) Skewed
index1 has unique values Unique
device.time_zone_offset_seconds has 708 (6.5%) zeros Zeros

Reproduction

Analysis started2021-04-09 23:42:51.039643
Analysis finished2021-04-09 23:44:19.136083
Duration1 minute and 28.1 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

event_date
Date

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.6 KiB
Minimum1970-01-01 00:00:20.201107
Maximum1970-01-01 00:00:20.201107
2021-04-09T16:44:19.298727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:19.525809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
Distinct10922
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size85.6 KiB
Minimum2020-11-07 07:01:29.326000
Maximum2020-11-08 06:59:31.517004
2021-04-09T16:44:19.768789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:20.048095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

event_name
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
user_engagement
4497 
screen_view
4070 
view_item
974 
search
802 
session_start
522 
Other values (5)
 
74
ValueCountFrequency (%) 
user_engagement449741.1%
 
screen_view407037.2%
 
view_item9748.9%
 
search8027.3%
 
session_start5224.8%
 
os_update570.5%
 
first_open100.1%
 
firebase_campaign3< 0.1%
 
app_update2< 0.1%
 
app_remove2< 0.1%
 
2021-04-09T16:44:20.336427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:20.502932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:23.058328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length11
Mean length12.18511747
Min length6

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e3354925.2%
 
n1359910.2%
 
s115278.6%
 
_101377.6%
 
r99067.4%
 
g89976.7%
 
t65844.9%
 
i65564.9%
 
a58934.4%
 
m54764.1%
 
v50463.8%
 
w50443.8%
 
c48753.7%
 
u45563.4%
 
h8020.6%
 
o5910.4%
 
p800.1%
 
d59< 0.1%
 
f13< 0.1%
 
b3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12315692.4%
 
Connector Punctuation101377.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3354927.2%
 
n1359911.0%
 
s115279.4%
 
r99068.0%
 
g89977.3%
 
t65845.3%
 
i65565.3%
 
a58934.8%
 
m54764.4%
 
v50464.1%
 
w50444.1%
 
c48754.0%
 
u45563.7%
 
h8020.7%
 
o5910.5%
 
p800.1%
 
d59< 0.1%
 
f13< 0.1%
 
b3< 0.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_10137100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12315692.4%
 
Common101377.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e3354927.2%
 
n1359911.0%
 
s115279.4%
 
r99068.0%
 
g89977.3%
 
t65845.3%
 
i65565.3%
 
a58934.8%
 
m54764.4%
 
v50464.1%
 
w50444.1%
 
c48754.0%
 
u45563.7%
 
h8020.7%
 
o5910.5%
 
p800.1%
 
d59< 0.1%
 
f13< 0.1%
 
b3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
_10137100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII133293100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e3354925.2%
 
n1359910.2%
 
s115278.6%
 
_101377.6%
 
r99067.4%
 
g89976.7%
 
t65844.9%
 
i65564.9%
 
a58934.4%
 
m54764.1%
 
v50463.8%
 
w50443.8%
 
c48753.7%
 
u45563.4%
 
h8020.6%
 
o5910.4%
 
p800.1%
 
d59< 0.1%
 
f13< 0.1%
 
b3< 0.1%
 
Distinct10839
Distinct (%)99.8%
Missing81
Missing (%)0.7%
Memory size85.6 KiB
Minimum2019-10-08 03:42:20.722001
Maximum2020-11-08 06:56:41.406004
2021-04-09T16:44:23.337579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:23.624412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

event_bundle_sequence_id
Real number (ℝ≥0)

Distinct1429
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1029.78892
Minimum1
Maximum6949
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:23.940232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q1205
median583
Q31292
95-th percentile3490.1
Maximum6949
Range6948
Interquartile range (IQR)1087

Descriptive statistics

Standard deviation1271.314172
Coefficient of variation (CV)1.2345386
Kurtosis5.974772455
Mean1029.78892
Median Absolute Deviation (MAD)463
Skewness2.261145022
Sum11264861
Variance1616239.724
MonotocityNot monotonic
2021-04-09T16:44:24.228072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5942552.3%
 
30041801.6%
 
1201681.5%
 
5951121.0%
 
822870.8%
 
1184850.8%
 
2810.7%
 
353710.6%
 
10710.6%
 
3710.6%
 
831710.6%
 
589700.6%
 
19680.6%
 
4630.6%
 
328620.6%
 
585620.6%
 
157580.5%
 
549530.5%
 
210500.5%
 
54500.5%
 
18490.4%
 
16490.4%
 
9480.4%
 
12480.4%
 
475470.4%
 
Other values (1404)891081.5%
 
ValueCountFrequency (%) 
1140.1%
 
2810.7%
 
3710.6%
 
4630.6%
 
5170.2%
 
6180.2%
 
7380.3%
 
8160.1%
 
9480.4%
 
10710.6%
 
ValueCountFrequency (%) 
69491< 0.1%
 
69481< 0.1%
 
69471< 0.1%
 
694680.1%
 
69451< 0.1%
 
69441< 0.1%
 
694370.1%
 
69421< 0.1%
 
69411< 0.1%
 
69401< 0.1%
 

event_server_timestamp_offset
Real number (ℝ≥0)

SKEWED

Distinct2252
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1344692.168
Minimum89
Maximum257000129
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:24.537891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile73322
Q1144027.5
median258026
Q3541230
95-th percentile2754937
Maximum257000129
Range257000040
Interquartile range (IQR)397202.5

Descriptive statistics

Standard deviation11256447.62
Coefficient of variation (CV)8.371021921
Kurtosis484.2141531
Mean1344692.168
Median Absolute Deviation (MAD)135684
Skewness21.54516149
Sum1.470958763e+10
Variance1.267076129e+14
MonotocityNot monotonic
2021-04-09T16:44:24.835743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7242382472.3%
 
3886961801.6%
 
128052221561.4%
 
724239950.9%
 
189630870.8%
 
7430374850.8%
 
2496478710.6%
 
337159610.6%
 
1570578590.5%
 
310108550.5%
 
145226530.5%
 
523632510.5%
 
885500.5%
 
318945490.4%
 
73322470.4%
 
156562460.4%
 
244412460.4%
 
73097430.4%
 
2754937420.4%
 
432121420.4%
 
226893410.4%
 
93994410.4%
 
208813410.4%
 
153438400.4%
 
164281400.4%
 
Other values (2227)917183.8%
 
ValueCountFrequency (%) 
8980.1%
 
1241< 0.1%
 
1321< 0.1%
 
1434< 0.1%
 
1964< 0.1%
 
247180.2%
 
2854< 0.1%
 
3055< 0.1%
 
3062< 0.1%
 
3341< 0.1%
 
ValueCountFrequency (%) 
2570001293< 0.1%
 
2570001282< 0.1%
 
2569971621< 0.1%
 
256997161140.1%
 
575311253< 0.1%
 
561685792< 0.1%
 
40321463100.1%
 
242308571< 0.1%
 
20149991190.2%
 
201499901< 0.1%
 

user_pseudo_id
Categorical

HIGH CARDINALITY

Distinct336
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size34.1 KiB
74327012A94B4A0997E880C1E84B0196
 
386
CA09CBD9DA064BAD8127E474DA810CA4
 
352
1214D69AD6D7496892C7E20641F912E4
 
242
EE2F93424ACA4D4DA534EEDEAAFFE385
 
227
C9CE33BB8BB44275BA44AF8A1E9A49B5
 
203
Other values (331)
9529 
ValueCountFrequency (%) 
74327012A94B4A0997E880C1E84B01963863.5%
 
CA09CBD9DA064BAD8127E474DA810CA43523.2%
 
1214D69AD6D7496892C7E20641F912E42422.2%
 
EE2F93424ACA4D4DA534EEDEAAFFE3852272.1%
 
C9CE33BB8BB44275BA44AF8A1E9A49B52031.9%
 
35BE2FE232044C37B5957CCE6544CB921841.7%
 
E6C7B5E7F3534584B3CEB13B3DF7A8BC1841.7%
 
48FC6D6A8B3240D7A23CFCAB3D5AE14E1801.6%
 
D51EC2F12C6E4CCA88686D83FB4931C21771.6%
 
fb212f55bde12d40c3a153b7e8ebaee51591.5%
 
6348431B9A0149C1923C994575DE10D21461.3%
 
2637605B5F5F4654923A17220CA419371181.1%
 
3C1D9DAC18C94F889E7DC30FB41D29061171.1%
 
360EA8DFAB9B4140881527097987A4B11131.0%
 
B3B1345BC574455D9345D3105791E3241121.0%
 
FAC32B2DF5344D00AC0A39C80DD9B2BD1081.0%
 
3D782D13ADD446DCADA3DF097A456AE31071.0%
 
c3d8031bfcd0730182cfb492f6775f621010.9%
 
8E536E9645C3431EB2817FBDAB102FBC1010.9%
 
25098FD2C1E34B25AE544A3AC0F768071000.9%
 
5B00B2D1E79E45A89D6DDB174A7DD22D950.9%
 
A711AD08E56445EF92AD9BEE977A2F20950.9%
 
0C9297A6E19440B79E781F7EE0090C91940.9%
 
229F64457AD441EB8097056318EBA422920.8%
 
A0C8450ACFFB4E2F9A1ECF717C3D4842880.8%
 
Other values (311)705864.5%
 
2021-04-09T16:44:25.127354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique4 ?
Unique (%)< 0.1%
2021-04-09T16:44:25.406361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length32
Median length32
Mean length32
Min length32

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
4334239.5%
 
9230686.6%
 
1226436.5%
 
A216716.2%
 
8213996.1%
 
7209496.0%
 
3206965.9%
 
0206165.9%
 
B204685.8%
 
2204335.8%
 
C201695.8%
 
5197765.6%
 
E197335.6%
 
D195535.6%
 
6182075.2%
 
F167244.8%
 
b20060.6%
 
f19540.6%
 
c18940.5%
 
e16630.5%
 
a15080.4%
 
d14950.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number22121063.2%
 
Uppercase Letter11831833.8%
 
Lowercase Letter105203.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
b200619.1%
 
f195418.6%
 
c189418.0%
 
e166315.8%
 
a150814.3%
 
d149514.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
43342315.1%
 
92306810.4%
 
12264310.2%
 
8213999.7%
 
7209499.5%
 
3206969.4%
 
0206169.3%
 
2204339.2%
 
5197768.9%
 
6182078.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A2167118.3%
 
B2046817.3%
 
C2016917.0%
 
E1973316.7%
 
D1955316.5%
 
F1672414.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common22121063.2%
 
Latin12883836.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A2167116.8%
 
B2046815.9%
 
C2016915.7%
 
E1973315.3%
 
D1955315.2%
 
F1672413.0%
 
b20061.6%
 
f19541.5%
 
c18941.5%
 
e16631.3%
 
a15081.2%
 
d14951.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
43342315.1%
 
92306810.4%
 
12264310.2%
 
8213999.7%
 
7209499.5%
 
3206969.4%
 
0206169.3%
 
2204339.2%
 
5197768.9%
 
6182078.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII350048100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
4334239.5%
 
9230686.6%
 
1226436.5%
 
A216716.2%
 
8213996.1%
 
7209496.0%
 
3206965.9%
 
0206165.9%
 
B204685.8%
 
2204335.8%
 
C201695.8%
 
5197765.6%
 
E197335.6%
 
D195535.6%
 
6182075.2%
 
F167244.8%
 
b20060.6%
 
f19540.6%
 
c18940.5%
 
e16630.5%
 
a15080.4%
 
d14950.4%
 
Distinct336
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size85.6 KiB
Minimum2017-11-21 04:03:35.882000
Maximum2020-11-08 02:48:50.131000
2021-04-09T16:44:25.640206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:25.917046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

stream_id
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.6 KiB
1080202923
10124 
1440534155
 
815
ValueCountFrequency (%) 
10802029231012492.5%
 
14405341558157.5%
 
2021-04-09T16:44:26.171097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:26.319012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:26.484919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
03118728.5%
 
23037227.8%
 
11175410.7%
 
31093910.0%
 
8101249.3%
 
9101249.3%
 
424452.2%
 
524452.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number109390100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03118728.5%
 
23037227.8%
 
11175410.7%
 
31093910.0%
 
8101249.3%
 
9101249.3%
 
424452.2%
 
524452.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common109390100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
03118728.5%
 
23037227.8%
 
11175410.7%
 
31093910.0%
 
8101249.3%
 
9101249.3%
 
424452.2%
 
524452.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII109390100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
03118728.5%
 
23037227.8%
 
11175410.7%
 
31093910.0%
 
8101249.3%
 
9101249.3%
 
424452.2%
 
524452.2%
 

platform
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
IOS
10124 
ANDROID
 
815
ValueCountFrequency (%) 
IOS1012492.5%
 
ANDROID8157.5%
 
2021-04-09T16:44:26.699795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:26.839718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:26.999334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.298016272
Min length3

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter36077100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin36077100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII36077100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

device.category
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
mobile
8998 
tablet
1941 
ValueCountFrequency (%) 
mobile899882.3%
 
tablet194117.7%
 
2021-04-09T16:44:27.242949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:27.411852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:27.579778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
b1093916.7%
 
l1093916.7%
 
e1093916.7%
 
m899813.7%
 
o899813.7%
 
i899813.7%
 
t38825.9%
 
a19413.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter65634100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
b1093916.7%
 
l1093916.7%
 
e1093916.7%
 
m899813.7%
 
o899813.7%
 
i899813.7%
 
t38825.9%
 
a19413.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin65634100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
b1093916.7%
 
l1093916.7%
 
e1093916.7%
 
m899813.7%
 
o899813.7%
 
i899813.7%
 
t38825.9%
 
a19413.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII65634100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
b1093916.7%
 
l1093916.7%
 
e1093916.7%
 
m899813.7%
 
o899813.7%
 
i899813.7%
 
t38825.9%
 
a19413.0%
 

device.mobile_brand_name
Categorical

MISSING

Distinct11
Distinct (%)0.1%
Missing242
Missing (%)2.2%
Memory size11.2 KiB
Apple
9882 
Samsung
 
435
OnePlus
 
174
Google
 
102
Vivo
 
32
Other values (6)
 
72
ValueCountFrequency (%) 
Apple988290.3%
 
Samsung4354.0%
 
OnePlus1741.6%
 
Google1020.9%
 
Vivo320.3%
 
Xiaomi200.2%
 
Motorola190.2%
 
Huawei140.1%
 
LG100.1%
 
OPPO70.1%
 
Amazon2< 0.1%
 
(Missing)2422.2%
 
2021-04-09T16:44:27.813623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:28.046489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length5
Mean length5.07861779
Min length2

Overview of Unicode Properties

Unique unicode characters26
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
p1976435.6%
 
l1017718.3%
 
e1017218.3%
 
A988417.8%
 
n10952.0%
 
a7321.3%
 
u6231.1%
 
s6091.1%
 
g5371.0%
 
m4570.8%
 
S4350.8%
 
o3150.6%
 
O1880.3%
 
P1880.3%
 
G1120.2%
 
i860.2%
 
V320.1%
 
v320.1%
 
X20< 0.1%
 
M19< 0.1%
 
t19< 0.1%
 
r19< 0.1%
 
H14< 0.1%
 
w14< 0.1%
 
L10< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4465380.4%
 
Uppercase Letter1090219.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A988490.7%
 
S4354.0%
 
O1881.7%
 
P1881.7%
 
G1121.0%
 
V320.3%
 
X200.2%
 
M190.2%
 
H140.1%
 
L100.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
p1976444.3%
 
l1017722.8%
 
e1017222.8%
 
n10952.5%
 
a7321.6%
 
u6231.4%
 
s6091.4%
 
g5371.2%
 
m4571.0%
 
o3150.7%
 
i860.2%
 
v320.1%
 
t19< 0.1%
 
r19< 0.1%
 
w14< 0.1%
 
z2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin55555100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
p1976435.6%
 
l1017718.3%
 
e1017218.3%
 
A988417.8%
 
n10952.0%
 
a7321.3%
 
u6231.1%
 
s6091.1%
 
g5371.0%
 
m4570.8%
 
S4350.8%
 
o3150.6%
 
O1880.3%
 
P1880.3%
 
G1120.2%
 
i860.2%
 
V320.1%
 
v320.1%
 
X20< 0.1%
 
M19< 0.1%
 
t19< 0.1%
 
r19< 0.1%
 
H14< 0.1%
 
w14< 0.1%
 
L10< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII55555100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
p1976435.6%
 
l1017718.3%
 
e1017218.3%
 
A988417.8%
 
n10952.0%
 
a7321.3%
 
u6231.1%
 
s6091.1%
 
g5371.0%
 
m4570.8%
 
S4350.8%
 
o3150.6%
 
O1880.3%
 
P1880.3%
 
G1120.2%
 
i860.2%
 
V320.1%
 
v320.1%
 
X20< 0.1%
 
M19< 0.1%
 
t19< 0.1%
 
r19< 0.1%
 
H14< 0.1%
 
w14< 0.1%
 
L10< 0.1%
 

device.mobile_model_name
Categorical

HIGH CARDINALITY
MISSING

Distinct70
Distinct (%)0.7%
Missing242
Missing (%)2.2%
Memory size13.9 KiB
iPhone
1550 
iPhone X
1250 
iPad
813 
iPhone 8 Plus
797 
iPhone XR
757 
Other values (65)
5530 
ValueCountFrequency (%) 
iPhone155014.2%
 
iPhone X125011.4%
 
iPad8137.4%
 
iPhone 8 Plus7977.3%
 
iPhone XR7576.9%
 
iPhone 76285.7%
 
iPhone 85334.9%
 
iPhone XS5234.8%
 
iPhone 7 Plus5054.6%
 
iPhone 6s4644.2%
 
iPhone XS Max4193.8%
 
iPad Pro 12.9 2nd gen2952.7%
 
iPad Pro 10.52542.3%
 
iPad Mini 42142.0%
 
iPhone 62081.9%
 
iPhone 6s Plus2061.9%
 
SM-S102DL1591.5%
 
iPad 6th gen1561.4%
 
A60031010.9%
 
iPhone SE970.9%
 
Pixel 2550.5%
 
SM-A105FN540.5%
 
iPad Air 2540.5%
 
iPad 5th gen530.5%
 
iPhone 6 Plus380.3%
 
Other values (45)5144.7%
 
(Missing)2422.2%
 
2021-04-09T16:44:28.339322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-04-09T16:44:28.645149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length8
Mean length8.973397934
Min length3

Overview of Unicode Properties

Unique unicode characters56
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
P1210312.3%
 
1129111.5%
 
i1055710.8%
 
n95139.7%
 
e86158.8%
 
o85868.7%
 
h82088.4%
 
X29583.0%
 
a25822.6%
 
s22162.3%
 
d22082.2%
 
l16641.7%
 
S16471.7%
 
u15461.6%
 
813411.4%
 
713311.4%
 
612411.3%
 
M11311.2%
 
010211.0%
 
19581.0%
 
28920.9%
 
R7770.8%
 
r6400.7%
 
.5590.6%
 
55520.6%
 
Other values (31)40234.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5765258.7%
 
Uppercase Letter1997120.3%
 
Space Separator1129111.5%
 
Decimal Number82228.4%
 
Other Punctuation5590.6%
 
Dash Punctuation4590.5%
 
Open Punctuation3< 0.1%
 
Close Punctuation3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P1210360.6%
 
X295814.8%
 
S16478.2%
 
M11315.7%
 
R7773.9%
 
A3181.6%
 
L1921.0%
 
D1590.8%
 
G1500.8%
 
F1240.6%
 
U1070.5%
 
E1060.5%
 
N960.5%
 
T460.2%
 
J170.1%
 
V100.1%
 
C7< 0.1%
 
H7< 0.1%
 
Z7< 0.1%
 
K4< 0.1%
 
W2< 0.1%
 
I2< 0.1%
 
B1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1055718.3%
 
n951316.5%
 
e861514.9%
 
o858614.9%
 
h820814.2%
 
a25824.5%
 
s22163.8%
 
d22083.8%
 
l16642.9%
 
u15462.7%
 
r6401.1%
 
x5210.9%
 
g5040.9%
 
t2280.4%
 
m290.1%
 
y16< 0.1%
 
c10< 0.1%
 
p9< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
8134116.3%
 
7133116.2%
 
6124115.1%
 
0102112.4%
 
195811.7%
 
289210.8%
 
55526.7%
 
94675.7%
 
42302.8%
 
31892.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-459100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
11291100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.559100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(3100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin7762379.1%
 
Common2053720.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
P1210315.6%
 
i1055713.6%
 
n951312.3%
 
e861511.1%
 
o858611.1%
 
h820810.6%
 
X29583.8%
 
a25823.3%
 
s22162.9%
 
d22082.8%
 
l16642.1%
 
S16472.1%
 
u15462.0%
 
M11311.5%
 
R7771.0%
 
r6400.8%
 
x5210.7%
 
g5040.6%
 
A3180.4%
 
t2280.3%
 
L1920.2%
 
D1590.2%
 
G1500.2%
 
F1240.2%
 
U1070.1%
 
Other values (16)3690.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
1129155.0%
 
813416.5%
 
713316.5%
 
612416.0%
 
010215.0%
 
19584.7%
 
28924.3%
 
.5592.7%
 
55522.7%
 
94672.3%
 
-4592.2%
 
42301.1%
 
31890.9%
 
(3< 0.1%
 
)3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII98160100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
P1210312.3%
 
1129111.5%
 
i1055710.8%
 
n95139.7%
 
e86158.8%
 
o85868.7%
 
h82088.4%
 
X29583.0%
 
a25822.6%
 
s22162.3%
 
d22082.2%
 
l16641.7%
 
S16471.7%
 
u15461.6%
 
813411.4%
 
713311.4%
 
612411.3%
 
M11311.2%
 
010211.0%
 
19581.0%
 
28920.9%
 
R7770.8%
 
r6400.7%
 
.5590.6%
 
55520.6%
 
Other values (31)40234.1%
 

device.mobile_os_hardware_model
Categorical

HIGH CARDINALITY

Distinct89
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size14.0 KiB
iPhone10,6
813 
iPhone11,8
 
757
iPhone9,3
 
595
iPhone12,1
 
535
iPhone11,2
 
523
Other values (84)
7716 
ValueCountFrequency (%) 
iPhone10,68137.4%
 
iPhone11,87576.9%
 
iPhone9,35955.4%
 
iPhone12,15354.9%
 
iPhone11,25234.8%
 
iPhone8,14644.2%
 
iPhone9,44494.1%
 
iPhone10,24414.0%
 
iPhone10,34374.0%
 
iPhone11,64193.8%
 
iPhone10,53563.3%
 
iPhone12,33553.2%
 
iPad11,33393.1%
 
iPhone10,43072.8%
 
iPhone12,83042.8%
 
iPad7,32542.3%
 
iPad7,112512.3%
 
x86_642422.2%
 
iPad8,12362.2%
 
iPhone10,12262.1%
 
iPad5,12142.0%
 
iPhone7,22081.9%
 
iPhone8,22061.9%
 
iPad11,12001.8%
 
iPhone12,51691.5%
 
Other values (64)163915.0%
 
2021-04-09T16:44:28.952141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-04-09T16:44:29.262601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length10
Mean length9.203126428
Min length5

Overview of Unicode Properties

Unique unicode characters53
Unique unicode categories9 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11169211.6%
 
P1014610.1%
 
i1003610.0%
 
,98829.8%
 
e81208.1%
 
o80598.0%
 
h79897.9%
 
n79897.9%
 
033553.3%
 
231433.1%
 
324962.5%
 
823332.3%
 
619621.9%
 
a19211.9%
 
d19131.9%
 
913031.3%
 
512031.2%
 
411811.2%
 
711391.1%
 
S7560.8%
 
M4810.5%
 
-4590.5%
 
x3440.3%
 
L3400.3%
 
3340.3%
 
Other values (28)20972.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4662646.3%
 
Decimal Number2980729.6%
 
Uppercase Letter1331713.2%
 
Other Punctuation98829.8%
 
Dash Punctuation4590.5%
 
Space Separator3340.3%
 
Connector Punctuation2420.2%
 
Open Punctuation3< 0.1%
 
Close Punctuation3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P1014676.2%
 
S7565.7%
 
M4813.6%
 
L3402.6%
 
U2561.9%
 
N2441.8%
 
A2371.8%
 
D1591.2%
 
G1501.1%
 
O1481.1%
 
E1481.1%
 
F1210.9%
 
T460.3%
 
R200.2%
 
J170.1%
 
X90.1%
 
C70.1%
 
H70.1%
 
V70.1%
 
Z70.1%
 
W5< 0.1%
 
K4< 0.1%
 
I2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1003621.5%
 
e812017.4%
 
o805917.3%
 
h798917.1%
 
n798917.1%
 
a19214.1%
 
d19134.1%
 
x3440.7%
 
l1180.3%
 
v640.1%
 
m290.1%
 
t19< 0.1%
 
y16< 0.1%
 
p9< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11169239.2%
 
0335511.3%
 
2314310.5%
 
324968.4%
 
823337.8%
 
619626.6%
 
913034.4%
 
512034.0%
 
411814.0%
 
711393.8%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_242100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-459100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
334100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,9882100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(3100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5994359.5%
 
Common4073040.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
P1014616.9%
 
i1003616.7%
 
e812013.5%
 
o805913.4%
 
h798913.3%
 
n798913.3%
 
a19213.2%
 
d19133.2%
 
S7561.3%
 
M4810.8%
 
x3440.6%
 
L3400.6%
 
U2560.4%
 
N2440.4%
 
A2370.4%
 
D1590.3%
 
G1500.3%
 
O1480.2%
 
E1480.2%
 
F1210.2%
 
l1180.2%
 
v640.1%
 
T460.1%
 
m29< 0.1%
 
R20< 0.1%
 
Other values (12)1090.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
11169228.7%
 
,988224.3%
 
033558.2%
 
231437.7%
 
324966.1%
 
823335.7%
 
619624.8%
 
913033.2%
 
512033.0%
 
411812.9%
 
711392.8%
 
-4591.1%
 
3340.8%
 
_2420.6%
 
(3< 0.1%
 
)3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII100673100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11169211.6%
 
P1014610.1%
 
i1003610.0%
 
,98829.8%
 
e81208.1%
 
o80598.0%
 
h79897.9%
 
n79897.9%
 
033553.3%
 
231433.1%
 
324962.5%
 
823332.3%
 
619621.9%
 
a19211.9%
 
d19131.9%
 
913031.3%
 
512031.2%
 
411811.2%
 
711391.1%
 
S7560.8%
 
M4810.5%
 
-4590.5%
 
x3440.3%
 
L3400.3%
 
3340.3%
 
Other values (28)20972.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
IOS
10124 
ANDROID
 
815
ValueCountFrequency (%) 
IOS1012492.5%
 
ANDROID8157.5%
 
2021-04-09T16:44:29.511460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:29.652400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:29.836274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length3
Mean length3.298016272
Min length3

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter36077100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin36077100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII36077100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
O1093930.3%
 
I1093930.3%
 
S1012428.1%
 
D16304.5%
 
A8152.3%
 
N8152.3%
 
R8152.3%
 
Distinct36
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
14.1
4202 
14.0.1
1234 
13.6.1
1146 
13.7
952 
14.2
681 
Other values (31)
2724 
ValueCountFrequency (%) 
14.1420238.4%
 
14.0.1123411.3%
 
13.6.1114610.5%
 
13.79528.7%
 
14.26816.2%
 
104734.3%
 
13.5.12782.5%
 
13.4.12442.2%
 
13.62362.2%
 
91981.8%
 
11.4.11841.7%
 
12.4.81481.4%
 
14.01331.2%
 
13.3.11191.1%
 
12.31081.0%
 
12.4.11081.0%
 
12.4.7950.9%
 
11870.8%
 
13.1.1540.5%
 
12.3.1340.3%
 
13.3290.3%
 
12.4.6270.2%
 
11.4240.2%
 
6.0.1200.2%
 
13.4190.2%
 
Other values (11)1061.0%
 
2021-04-09T16:44:30.119112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:30.370971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.526099278
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
11869837.8%
 
.1394428.2%
 
4713514.4%
 
334066.9%
 
019363.9%
 
614292.9%
 
212492.5%
 
710612.1%
 
52780.6%
 
92040.4%
 
81710.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3556771.8%
 
Other Punctuation1394428.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11869852.6%
 
4713520.1%
 
334069.6%
 
019365.4%
 
614294.0%
 
212493.5%
 
710613.0%
 
52780.8%
 
92040.6%
 
81710.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.13944100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common49511100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
11869837.8%
 
.1394428.2%
 
4713514.4%
 
334066.9%
 
019363.9%
 
614292.9%
 
212492.5%
 
710612.1%
 
52780.6%
 
92040.4%
 
81710.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII49511100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
11869837.8%
 
.1394428.2%
 
4713514.4%
 
334066.9%
 
019363.9%
 
614292.9%
 
212492.5%
 
710612.1%
 
52780.6%
 
92040.4%
 
81710.3%
 

device.language
Categorical

Distinct37
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
en-us
5907 
en-ph
1842 
en-gb
 
397
en-id
 
386
en-au
 
317
Other values (32)
2090 
ValueCountFrequency (%) 
en-us590754.0%
 
en-ph184216.8%
 
en-gb3973.6%
 
en-id3863.5%
 
en-au3172.9%
 
en-ca2942.7%
 
en2772.5%
 
en-th2442.2%
 
de-us1981.8%
 
en-vn1851.7%
 
en-sg1341.2%
 
en-de1171.1%
 
en-nl800.7%
 
en-jp750.7%
 
en-my660.6%
 
en-sa590.5%
 
zh-hant-hk570.5%
 
en-es560.5%
 
en-bz540.5%
 
zh-hant-tw300.3%
 
en-ie240.2%
 
en-za180.2%
 
en-hk160.1%
 
zh-hans-ph150.1%
 
en-ru140.1%
 
Other values (12)770.7%
 
2021-04-09T16:44:30.646813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-04-09T16:44:30.904907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length5
Mean length4.973397934
Min length2

Overview of Unicode Properties

Unique unicode characters24
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1099620.2%
 
n1096020.1%
 
-1077019.8%
 
u647011.9%
 
s642711.8%
 
h24164.4%
 
p19323.6%
 
a7961.5%
 
d7091.3%
 
g5311.0%
 
b4510.8%
 
i4110.8%
 
t3820.7%
 
c3000.6%
 
v1880.3%
 
z1800.3%
 
k810.1%
 
l800.1%
 
j750.1%
 
m660.1%
 
y660.1%
 
r560.1%
 
w390.1%
 
f22< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4363480.2%
 
Dash Punctuation1077019.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1099625.2%
 
n1096025.1%
 
u647014.8%
 
s642714.7%
 
h24165.5%
 
p19324.4%
 
a7961.8%
 
d7091.6%
 
g5311.2%
 
b4511.0%
 
i4110.9%
 
t3820.9%
 
c3000.7%
 
v1880.4%
 
z1800.4%
 
k810.2%
 
l800.2%
 
j750.2%
 
m660.2%
 
y660.2%
 
r560.1%
 
w390.1%
 
f220.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-10770100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4363480.2%
 
Common1077019.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1099625.2%
 
n1096025.1%
 
u647014.8%
 
s642714.7%
 
h24165.5%
 
p19324.4%
 
a7961.8%
 
d7091.6%
 
g5311.2%
 
b4511.0%
 
i4110.9%
 
t3820.9%
 
c3000.7%
 
v1880.4%
 
z1800.4%
 
k810.2%
 
l800.2%
 
j750.2%
 
m660.2%
 
y660.2%
 
r560.1%
 
w390.1%
 
f220.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
-10770100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54404100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1099620.2%
 
n1096020.1%
 
-1077019.8%
 
u647011.9%
 
s642711.8%
 
h24164.4%
 
p19323.6%
 
a7961.5%
 
d7091.3%
 
g5311.0%
 
b4510.8%
 
i4110.8%
 
t3820.7%
 
c3000.6%
 
v1880.3%
 
z1800.3%
 
k810.1%
 
l800.1%
 
j750.1%
 
m660.1%
 
y660.1%
 
r560.1%
 
w390.1%
 
f22< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Yes
6275 
No
4664 
ValueCountFrequency (%) 
Yes627557.4%
 
No466442.6%
 
2021-04-09T16:44:31.045522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

device.time_zone_offset_seconds
Real number (ℝ)

ZEROS

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2562.519426
Minimum-36000
Maximum46800
Zeros708
Zeros (%)6.5%
Memory size85.6 KiB
2021-04-09T16:44:31.214300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-36000
5-th percentile-28800
Q1-21600
median-18000
Q328800
95-th percentile28800
Maximum46800
Range82800
Interquartile range (IQR)50400

Descriptive statistics

Standard deviation24835.0281
Coefficient of variation (CV)-9.691644812
Kurtosis-1.629358592
Mean-2562.519426
Median Absolute Deviation (MAD)10800
Skewness0.384075817
Sum-28031400
Variance616778620.7
MonotocityNot monotonic
2021-04-09T16:44:31.423162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%) 
28800252723.1%
 
-28800211919.4%
 
-21600177416.2%
 
-18000160514.7%
 
252008778.0%
 
07086.5%
 
-252005745.2%
 
324002412.2%
 
360001221.1%
 
36001131.0%
 
10800760.7%
 
39600720.7%
 
46800650.6%
 
14400350.3%
 
7200200.2%
 
-3600070.1%
 
180003< 0.1%
 
198001< 0.1%
 
ValueCountFrequency (%) 
-3600070.1%
 
-28800211919.4%
 
-252005745.2%
 
-21600177416.2%
 
-18000160514.7%
 
07086.5%
 
36001131.0%
 
7200200.2%
 
10800760.7%
 
14400350.3%
 
ValueCountFrequency (%) 
46800650.6%
 
39600720.7%
 
360001221.1%
 
324002412.2%
 
28800252723.1%
 
252008778.0%
 
198001< 0.1%
 
180003< 0.1%
 
14400350.3%
 
10800760.7%
 

geo.continent
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Americas
6079 
Asia
3701 
Europe
838 
Oceania
 
259
Africa
 
62
ValueCountFrequency (%) 
Americas607955.6%
 
Asia370133.8%
 
Europe8387.7%
 
Oceania2592.4%
 
Africa620.6%
 
2021-04-09T16:44:31.682011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:31.851914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:32.127567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length6.458451412
Min length4

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1036014.7%
 
i1010114.3%
 
A984213.9%
 
s978013.8%
 
e717610.2%
 
r69799.9%
 
c64009.1%
 
m60798.6%
 
E8381.2%
 
u8381.2%
 
o8381.2%
 
p8381.2%
 
O2590.4%
 
n2590.4%
 
f620.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5971084.5%
 
Uppercase Letter1093915.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A984290.0%
 
E8387.7%
 
O2592.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1036017.4%
 
i1010116.9%
 
s978016.4%
 
e717612.0%
 
r697911.7%
 
c640010.7%
 
m607910.2%
 
u8381.4%
 
o8381.4%
 
p8381.4%
 
n2590.4%
 
f620.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin70649100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1036014.7%
 
i1010114.3%
 
A984213.9%
 
s978013.8%
 
e717610.2%
 
r69799.9%
 
c64009.1%
 
m60798.6%
 
E8381.2%
 
u8381.2%
 
o8381.2%
 
p8381.2%
 
O2590.4%
 
n2590.4%
 
f620.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII70649100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1036014.7%
 
i1010114.3%
 
A984213.9%
 
s978013.8%
 
e717610.2%
 
r69799.9%
 
c64009.1%
 
m60798.6%
 
E8381.2%
 
u8381.2%
 
o8381.2%
 
p8381.2%
 
O2590.4%
 
n2590.4%
 
f620.1%
 

geo.country
Categorical

Distinct27
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
United States
5487 
Philippines
2126 
United Kingdom
687 
Canada
 
538
Thailand
 
452
Other values (22)
1649 
ValueCountFrequency (%) 
United States548750.2%
 
Philippines212619.4%
 
United Kingdom6876.3%
 
Canada5384.9%
 
Thailand4524.1%
 
Indonesia3863.5%
 
South Korea2412.2%
 
Australia1661.5%
 
Singapore1341.2%
 
Germany950.9%
 
Hong Kong870.8%
 
Malaysia760.7%
 
New Zealand650.6%
 
Ethiopia620.6%
 
Vietnam610.6%
 
Belize540.5%
 
Taiwan530.5%
 
Oman350.3%
 
China290.3%
 
Guam280.3%
 
Ireland210.2%
 
Israel200.2%
 
Russia170.2%
 
Denmark80.1%
 
Croatia70.1%
 
Other values (2)4< 0.1%
 
2021-04-09T16:44:32.422422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-04-09T16:44:32.688250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length13
Mean length11.44839565
Min length4

Overview of Unicode Properties

Unique unicode characters39
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t1768514.1%
 
e1499712.0%
 
i1479911.8%
 
n114289.1%
 
a99537.9%
 
d83276.6%
 
s82956.6%
 
65675.2%
 
U61744.9%
 
S58654.7%
 
p44483.6%
 
l29802.4%
 
h29102.3%
 
P21261.7%
 
o19321.5%
 
K10150.8%
 
g9950.8%
 
m9140.7%
 
r6920.6%
 
C5740.5%
 
T5050.4%
 
u4520.4%
 
I4280.3%
 
y1710.1%
 
A1660.1%
 
Other values (14)8360.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10116180.8%
 
Uppercase Letter1750614.0%
 
Space Separator65675.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U617435.3%
 
S586533.5%
 
P212612.1%
 
K10155.8%
 
C5743.3%
 
T5052.9%
 
I4282.4%
 
A1660.9%
 
G1230.7%
 
H870.5%
 
M760.4%
 
N650.4%
 
Z650.4%
 
E620.4%
 
V610.3%
 
B540.3%
 
O350.2%
 
R170.1%
 
D8< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t1768517.5%
 
e1499714.8%
 
i1479914.6%
 
n1142811.3%
 
a99539.8%
 
d83278.2%
 
s82958.2%
 
p44484.4%
 
l29802.9%
 
h29102.9%
 
o19321.9%
 
g9951.0%
 
m9140.9%
 
r6920.7%
 
u4520.4%
 
y1710.2%
 
w1210.1%
 
z540.1%
 
k8< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6567100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin11866794.8%
 
Common65675.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t1768514.9%
 
e1499712.6%
 
i1479912.5%
 
n114289.6%
 
a99538.4%
 
d83277.0%
 
s82957.0%
 
U61745.2%
 
S58654.9%
 
p44483.7%
 
l29802.5%
 
h29102.5%
 
P21261.8%
 
o19321.6%
 
K10150.9%
 
g9950.8%
 
m9140.8%
 
r6920.6%
 
C5740.5%
 
T5050.4%
 
u4520.4%
 
I4280.4%
 
y1710.1%
 
A1660.1%
 
G1230.1%
 
Other values (13)7130.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
6567100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII125234100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t1768514.1%
 
e1499712.0%
 
i1479911.8%
 
n114289.1%
 
a99537.9%
 
d83276.6%
 
s82956.6%
 
65675.2%
 
U61744.9%
 
S58654.7%
 
p44483.6%
 
l29802.4%
 
h29102.3%
 
P21261.7%
 
o19321.5%
 
K10150.8%
 
g9950.8%
 
m9140.7%
 
r6920.6%
 
C5740.5%
 
T5050.4%
 
u4520.4%
 
I4280.3%
 
y1710.1%
 
A1660.1%
 
Other values (14)8360.7%
 

geo.region
Categorical

HIGH CARDINALITY
MISSING

Distinct70
Distinct (%)0.7%
Missing288
Missing (%)2.6%
Memory size13.9 KiB
California
1806 
Texas
1105 
Metro Manila
882 
England
687 
Central Visayas
 
465
Other values (65)
5706 
ValueCountFrequency (%) 
California180616.5%
 
Texas110510.1%
 
Metro Manila8828.1%
 
England6876.3%
 
Central Visayas4654.3%
 
Bangkok4444.1%
 
Jakarta3863.5%
 
Massachusetts3823.5%
 
New York3583.3%
 
Florida3283.0%
 
Davao Region2932.7%
 
Arizona2912.7%
 
Illinois2582.4%
 
Pennsylvania2572.3%
 
Gyeonggi-do2272.1%
 
Alberta2212.0%
 
Central Luzon2202.0%
 
Calabarzon1671.5%
 
Virginia1491.4%
 
Ontario1441.3%
 
British Columbia1291.2%
 
Washington1251.1%
 
Hessen950.9%
 
Queensland940.9%
 
Maryland750.7%
 
Other values (45)10639.7%
 
(Missing)2882.6%
 
2021-04-09T16:44:32.967110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2021-04-09T16:44:33.250954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length10
Mean length9.320870281
Min length3

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1582615.5%
 
n95149.3%
 
i89928.8%
 
o73017.2%
 
l62896.2%
 
r62036.1%
 
e56945.6%
 
s50364.9%
 
t40073.9%
 
32373.2%
 
C30393.0%
 
M23822.3%
 
g23442.3%
 
f18571.8%
 
k17701.7%
 
d16921.7%
 
y12601.2%
 
u12331.2%
 
T12211.2%
 
x11051.1%
 
h10091.0%
 
b7230.7%
 
E6870.7%
 
z6780.7%
 
A6760.7%
 
Other values (26)81868.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8464683.0%
 
Uppercase Letter1384413.6%
 
Space Separator32373.2%
 
Dash Punctuation2340.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C303922.0%
 
M238217.2%
 
T12218.8%
 
E6875.0%
 
A6764.9%
 
V6154.4%
 
B5734.1%
 
N5063.7%
 
J4012.9%
 
R3852.8%
 
I3822.8%
 
D3722.7%
 
F3712.7%
 
Y3582.6%
 
G2772.0%
 
L2722.0%
 
P2712.0%
 
O2681.9%
 
W2631.9%
 
H1631.2%
 
S1461.1%
 
Q1381.0%
 
K580.4%
 
Z200.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1582618.7%
 
n951411.2%
 
i899210.6%
 
o73018.6%
 
l62897.4%
 
r62037.3%
 
e56946.7%
 
s50365.9%
 
t40074.7%
 
g23442.8%
 
f18572.2%
 
k17702.1%
 
d16922.0%
 
y12601.5%
 
u12331.5%
 
x11051.3%
 
h10091.2%
 
b7230.9%
 
z6780.8%
 
v6510.8%
 
c6210.7%
 
w4800.6%
 
m2500.3%
 
p960.1%
 
j15< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3237100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-234100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin9849096.6%
 
Common34713.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1582616.1%
 
n95149.7%
 
i89929.1%
 
o73017.4%
 
l62896.4%
 
r62036.3%
 
e56945.8%
 
s50365.1%
 
t40074.1%
 
C30393.1%
 
M23822.4%
 
g23442.4%
 
f18571.9%
 
k17701.8%
 
d16921.7%
 
y12601.3%
 
u12331.3%
 
T12211.2%
 
x11051.1%
 
h10091.0%
 
b7230.7%
 
E6870.7%
 
z6780.7%
 
A6760.7%
 
v6510.7%
 
Other values (24)73017.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
323793.3%
 
-2346.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII101961100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1582615.5%
 
n95149.3%
 
i89928.8%
 
o73017.2%
 
l62896.2%
 
r62036.1%
 
e56945.6%
 
s50364.9%
 
t40073.9%
 
32373.2%
 
C30393.0%
 
M23822.3%
 
g23442.3%
 
f18571.8%
 
k17701.7%
 
d16921.7%
 
y12601.2%
 
u12331.2%
 
T12211.2%
 
x11051.1%
 
h10091.0%
 
b7230.7%
 
E6870.7%
 
z6780.7%
 
A6760.7%
 
Other values (26)81868.0%
 

geo.city
Categorical

HIGH CARDINALITY
MISSING

Distinct155
Distinct (%)1.4%
Missing186
Missing (%)1.7%
Memory size27.7 KiB
Makati
 
614
Cebu City
 
465
Bangkok
 
444
Los Angeles
 
438
Jakarta
 
386
Other values (150)
8406 
ValueCountFrequency (%) 
Makati6145.6%
 
Cebu City4654.3%
 
Bangkok4444.1%
 
Los Angeles4384.0%
 
Jakarta3863.5%
 
Chelmsford3613.3%
 
Manchester3263.0%
 
Davao City2792.6%
 
Tempe2782.5%
 
Palm Harbor2572.3%
 
New York2412.2%
 
Anaheim2332.1%
 
Boston2312.1%
 
Houston2312.1%
 
Seongnam-si2272.1%
 
(not set)2262.1%
 
Georgetown2031.9%
 
Calgary2001.8%
 
Denton1961.8%
 
Riverside1671.5%
 
Austin1621.5%
 
Washington1591.5%
 
Cambridge1511.4%
 
Hamilton1351.2%
 
Singapore1341.2%
 
Other values (130)400936.6%
 
(Missing)1861.7%
 
2021-04-09T16:44:33.549783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2021-04-09T16:44:33.836641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length8
Mean length8.363927233
Min length3

Overview of Unicode Properties

Unique unicode characters54
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1012911.1%
 
e74378.1%
 
n73968.1%
 
o62246.8%
 
i60036.6%
 
t55646.1%
 
s41874.6%
 
r41594.5%
 
l34343.8%
 
32143.5%
 
g27943.1%
 
C26832.9%
 
k22822.5%
 
h20472.2%
 
m18802.1%
 
u17281.9%
 
d17261.9%
 
y15511.7%
 
A13261.4%
 
b13181.4%
 
M11341.2%
 
c10661.2%
 
B10481.1%
 
S10321.1%
 
v10261.1%
 
Other values (29)910510.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7403180.9%
 
Uppercase Letter1354914.8%
 
Space Separator32143.5%
 
Dash Punctuation2470.3%
 
Open Punctuation2260.2%
 
Close Punctuation2260.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C268319.8%
 
A13269.8%
 
M11348.4%
 
B10487.7%
 
S10327.6%
 
H9246.8%
 
D7675.7%
 
P6464.8%
 
L6364.7%
 
T5153.8%
 
J4303.2%
 
N3772.8%
 
R3732.8%
 
W2752.0%
 
Y2692.0%
 
G2031.5%
 
V1771.3%
 
O1571.2%
 
E1240.9%
 
U1110.8%
 
Q980.7%
 
I900.7%
 
F690.5%
 
K650.5%
 
Z200.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1012913.7%
 
e743710.0%
 
n739610.0%
 
o62248.4%
 
i60038.1%
 
t55647.5%
 
s41875.7%
 
r41595.6%
 
l34344.6%
 
g27943.8%
 
k22823.1%
 
h20472.8%
 
m18802.5%
 
u17282.3%
 
d17262.3%
 
y15512.1%
 
b13181.8%
 
c10661.4%
 
v10261.4%
 
p8801.2%
 
w5020.7%
 
f4450.6%
 
z1700.2%
 
q550.1%
 
x28< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3214100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-247100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(226100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)226100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8758095.7%
 
Common39134.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1012911.6%
 
e74378.5%
 
n73968.4%
 
o62247.1%
 
i60036.9%
 
t55646.4%
 
s41874.8%
 
r41594.7%
 
l34343.9%
 
g27943.2%
 
C26833.1%
 
k22822.6%
 
h20472.3%
 
m18802.1%
 
u17282.0%
 
d17262.0%
 
y15511.8%
 
A13261.5%
 
b13181.5%
 
M11341.3%
 
c10661.2%
 
B10481.2%
 
S10321.2%
 
v10261.2%
 
H9241.1%
 
Other values (25)74828.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
321482.1%
 
-2476.3%
 
(2265.8%
 
)2265.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII91493100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1012911.1%
 
e74378.1%
 
n73968.1%
 
o62246.8%
 
i60036.6%
 
t55646.1%
 
s41874.6%
 
r41594.5%
 
l34343.8%
 
32143.5%
 
g27943.1%
 
C26832.9%
 
k22822.5%
 
h20472.2%
 
m18802.1%
 
u17281.9%
 
d17261.9%
 
y15511.7%
 
A13261.4%
 
b13181.4%
 
M11341.2%
 
c10661.2%
 
B10481.1%
 
S10321.1%
 
v10261.1%
 
Other values (29)910510.0%
 
Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Northern America
6025 
Southeast Asia
3235 
Northern Europe
719 
Eastern Asia
 
410
Australasia
 
231
Other values (8)
 
319
ValueCountFrequency (%) 
Northern America602555.1%
 
Southeast Asia323529.6%
 
Northern Europe7196.6%
 
Eastern Asia4103.7%
 
Australasia2312.1%
 
Western Europe950.9%
 
Eastern Africa620.6%
 
Western Asia550.5%
 
Central America540.5%
 
Micronesian Region280.3%
 
Eastern Europe170.2%
 
Southern Europe70.1%
 
Southern Asia1< 0.1%
 
2021-04-09T16:44:34.103469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-04-09T16:44:34.359321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length18
Median length16
Mean length15.03464668
Min length11

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r2142713.0%
 
e1780310.8%
 
a143418.7%
 
t141468.6%
 
o108816.6%
 
107086.5%
 
i101576.2%
 
A100736.1%
 
h99876.1%
 
s80654.9%
 
n75294.6%
 
N67444.1%
 
c61693.8%
 
m60793.7%
 
u43122.6%
 
S32432.0%
 
E13270.8%
 
p8380.5%
 
l2850.2%
 
W1500.1%
 
f62< 0.1%
 
C54< 0.1%
 
M28< 0.1%
 
R28< 0.1%
 
g28< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter13210980.3%
 
Uppercase Letter2164713.2%
 
Space Separator107086.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1007346.5%
 
N674431.2%
 
S324315.0%
 
E13276.1%
 
W1500.7%
 
C540.2%
 
M280.1%
 
R280.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r2142716.2%
 
e1780313.5%
 
a1434110.9%
 
t1414610.7%
 
o108818.2%
 
i101577.7%
 
h99877.6%
 
s80656.1%
 
n75295.7%
 
c61694.7%
 
m60794.6%
 
u43123.3%
 
p8380.6%
 
l2850.2%
 
f62< 0.1%
 
g28< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10708100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin15375693.5%
 
Common107086.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r2142713.9%
 
e1780311.6%
 
a143419.3%
 
t141469.2%
 
o108817.1%
 
i101576.6%
 
A100736.6%
 
h99876.5%
 
s80655.2%
 
n75294.9%
 
N67444.4%
 
c61694.0%
 
m60794.0%
 
u43122.8%
 
S32432.1%
 
E13270.9%
 
p8380.5%
 
l2850.2%
 
W1500.1%
 
f62< 0.1%
 
C54< 0.1%
 
M28< 0.1%
 
R28< 0.1%
 
g28< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
10708100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII164464100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r2142713.0%
 
e1780310.8%
 
a143418.7%
 
t141468.6%
 
o108816.6%
 
107086.5%
 
i101576.2%
 
A100736.1%
 
h99876.1%
 
s80654.9%
 
n75294.6%
 
N67444.1%
 
c61693.8%
 
m60793.7%
 
u43122.6%
 
S32432.0%
 
E13270.8%
 
p8380.5%
 
l2850.2%
 
W1500.1%
 
f62< 0.1%
 
C54< 0.1%
 
M28< 0.1%
 
R28< 0.1%
 
g28< 0.1%
 

app_info.version
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
1.5.7
9800 
1.1.7
 
795
1.5.3
 
161
1.5.6
 
104
1.5.5
 
29
Other values (4)
 
50
ValueCountFrequency (%) 
1.5.7980089.6%
 
1.1.77957.3%
 
1.5.31611.5%
 
1.5.61041.0%
 
1.5.5290.3%
 
1.5.2270.2%
 
1.1.3130.1%
 
1.1.6.170.1%
 
1.3.23< 0.1%
 
2021-04-09T16:44:34.622171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:34.806067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:35.107393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length5
Mean length5.001279824
Min length5

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.2188540.0%
 
11176121.5%
 
71059519.4%
 
51015018.6%
 
31770.3%
 
61110.2%
 
2300.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3282460.0%
 
Other Punctuation2188540.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
11176135.8%
 
71059532.3%
 
51015030.9%
 
31770.5%
 
61110.3%
 
2300.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.21885100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common54709100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.2188540.0%
 
11176121.5%
 
71059519.4%
 
51015018.6%
 
31770.3%
 
61110.2%
 
2300.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54709100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.2188540.0%
 
11176121.5%
 
71059519.4%
 
51015018.6%
 
31770.3%
 
61110.2%
 
2300.1%
 
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
iTunes
9882 
com.android.vending
 
814
manual_install
 
242
com.sec.android.easyMover
 
1
ValueCountFrequency (%) 
iTunes988290.3%
 
com.android.vending8147.4%
 
manual_install2422.2%
 
com.sec.android.easyMover1< 0.1%
 
2021-04-09T16:44:35.370541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-04-09T16:44:35.534430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:35.771313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length6
Mean length7.146082823
Min length6

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1280916.4%
 
i1175315.0%
 
e1069913.7%
 
s1012613.0%
 
u1012413.0%
 
T988212.6%
 
d24443.1%
 
o16312.1%
 
.16312.1%
 
a15422.0%
 
m10571.4%
 
c8161.0%
 
r8161.0%
 
v8151.0%
 
g8141.0%
 
l7260.9%
 
_2420.3%
 
t2420.3%
 
y1< 0.1%
 
M1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6641585.0%
 
Uppercase Letter988312.6%
 
Other Punctuation16312.1%
 
Connector Punctuation2420.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1280919.3%
 
i1175317.7%
 
e1069916.1%
 
s1012615.2%
 
u1012415.2%
 
d24443.7%
 
o16312.5%
 
a15422.3%
 
m10571.6%
 
c8161.2%
 
r8161.2%
 
v8151.2%
 
g8141.2%
 
l7261.1%
 
t2420.4%
 
y1< 0.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.1631100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_242100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T9882> 99.9%
 
M1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin7629897.6%
 
Common18732.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1280916.8%
 
i1175315.4%
 
e1069914.0%
 
s1012613.3%
 
u1012413.3%
 
T988213.0%
 
d24443.2%
 
o16312.1%
 
a15422.0%
 
m10571.4%
 
c8161.1%
 
r8161.1%
 
v8151.1%
 
g8141.1%
 
l7261.0%
 
t2420.3%
 
y1< 0.1%
 
M1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
.163187.1%
 
_24212.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII78171100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1280916.4%
 
i1175315.0%
 
e1069913.7%
 
s1012613.0%
 
u1012413.0%
 
T988212.6%
 
d24443.1%
 
o16312.1%
 
.16312.1%
 
a15422.0%
 
m10571.4%
 
c8161.0%
 
r8161.0%
 
v8151.0%
 
g8141.0%
 
l7260.9%
 
_2420.3%
 
t2420.3%
 
y1< 0.1%
 
M1< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
(none)
10261 
organic
 
678
ValueCountFrequency (%) 
(none)1026193.8%
 
organic6786.2%
 
2021-04-09T16:44:36.012158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:36.150079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:36.323980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length6
Mean length6.061980071
Min length6

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2120032.0%
 
o1093916.5%
 
(1026115.5%
 
e1026115.5%
 
)1026115.5%
 
r6781.0%
 
g6781.0%
 
a6781.0%
 
i6781.0%
 
c6781.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4579069.1%
 
Open Punctuation1026115.5%
 
Close Punctuation1026115.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2120046.3%
 
o1093923.9%
 
e1026122.4%
 
r6781.5%
 
g6781.5%
 
a6781.5%
 
i6781.5%
 
c6781.5%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(10261100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)10261100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4579069.1%
 
Common2052230.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2120046.3%
 
o1093923.9%
 
e1026122.4%
 
r6781.5%
 
g6781.5%
 
a6781.5%
 
i6781.5%
 
c6781.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
(1026150.0%
 
)1026150.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII66312100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2120032.0%
 
o1093916.5%
 
(1026115.5%
 
e1026115.5%
 
)1026115.5%
 
r6781.0%
 
g6781.0%
 
a6781.0%
 
i6781.0%
 
c6781.0%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
(direct)
10261 
google-play
 
678
ValueCountFrequency (%) 
(direct)1026193.8%
 
google-play6786.2%
 
2021-04-09T16:44:36.549853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:36.699766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:36.872656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length8
Mean length8.185940214
Min length8

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e1093912.2%
 
(1026111.5%
 
d1026111.5%
 
i1026111.5%
 
r1026111.5%
 
c1026111.5%
 
t1026111.5%
 
)1026111.5%
 
g13561.5%
 
o13561.5%
 
l13561.5%
 
-6780.8%
 
p6780.8%
 
a6780.8%
 
y6780.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6834676.3%
 
Open Punctuation1026111.5%
 
Close Punctuation1026111.5%
 
Dash Punctuation6780.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e1093916.0%
 
d1026115.0%
 
i1026115.0%
 
r1026115.0%
 
c1026115.0%
 
t1026115.0%
 
g13562.0%
 
o13562.0%
 
l13562.0%
 
p6781.0%
 
a6781.0%
 
y6781.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-678100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(10261100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)10261100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6834676.3%
 
Common2120023.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e1093916.0%
 
d1026115.0%
 
i1026115.0%
 
r1026115.0%
 
c1026115.0%
 
t1026115.0%
 
g13562.0%
 
o13562.0%
 
l13562.0%
 
p6781.0%
 
a6781.0%
 
y6781.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
(1026148.4%
 
)1026148.4%
 
-6783.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII89546100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e1093912.2%
 
(1026111.5%
 
d1026111.5%
 
i1026111.5%
 
r1026111.5%
 
c1026111.5%
 
t1026111.5%
 
)1026111.5%
 
g13561.5%
 
o13561.5%
 
l13561.5%
 
-6780.8%
 
p6780.8%
 
a6780.8%
 
y6780.8%
 

device.vendor_id
Categorical

HIGH CARDINALITY
MISSING

Distinct281
Distinct (%)2.8%
Missing1013
Missing (%)9.3%
Memory size33.7 KiB
518F9ACB-2EF9-4D04-95D0-F8E2934B6C56
 
386
F40E12DE-09EC-4681-8C2B-0DB7615DD294
 
352
7F55BF8C-408E-4824-B656-57382B20F178
 
242
61DD3D7D-184E-498C-B0DB-49EEEAEE76DF
 
227
42F41601-976E-4501-BF55-122C6405A9F0
 
203
Other values (276)
8516 
ValueCountFrequency (%) 
518F9ACB-2EF9-4D04-95D0-F8E2934B6C563863.5%
 
F40E12DE-09EC-4681-8C2B-0DB7615DD2943523.2%
 
7F55BF8C-408E-4824-B656-57382B20F1782422.2%
 
61DD3D7D-184E-498C-B0DB-49EEEAEE76DF2272.1%
 
42F41601-976E-4501-BF55-122C6405A9F02031.9%
 
D4651C75-887D-4630-B6F0-D7D3216F36EB1841.7%
 
D8D31736-2EF9-44B8-998D-5A809AFF0B611801.6%
 
01BF88A6-8D2D-4815-A06A-616609FB423F1771.6%
 
0F176C9A-4912-441B-BE3D-586E2A4FECF61461.3%
 
EC530B33-BDE3-4D14-99E7-7F3131E5F9541181.1%
 
9FD44287-A5EA-46A4-8BA9-FA115FB0EB2C1171.1%
 
1C3CEE92-C2A3-463D-BA8B-54C4E586C5781131.0%
 
6643DCB4-9CCC-4CDC-ACD3-8AC36FA964B01121.0%
 
40FF0037-16C6-4F02-AD7E-4B158522CB4A1081.0%
 
683EF452-BF98-4733-8E6C-A240346DB45E1071.0%
 
94356B07-04BB-40E7-87C9-BCCE1BDBF2491010.9%
 
0A8E3008-0EA3-4996-A598-D5AD2D3B36151000.9%
 
6766C70D-4394-4448-8AC6-15DE5FFE0993950.9%
 
C7648C61-3B13-49BE-A050-9F42E11F8F18950.9%
 
DAB2BC4E-084C-4C0F-98E5-C7C0086C7E80940.9%
 
8B52B9DE-CC36-4A31-B7FD-CE052B20AE16920.8%
 
61709D69-8F40-4CC3-920C-5B7C1BFC007D880.8%
 
9AABA4F9-8A67-4B7C-BFE2-654B9D99CEEC880.8%
 
3626C313-C44B-4CD0-86CE-7B729817A599860.8%
 
BF15A722-B163-4A52-9F9E-2A04E75DBCEF840.8%
 
Other values (256)623157.0%
 
(Missing)10139.3%
 
2021-04-09T16:44:37.155305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2021-04-09T16:44:37.460133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length32.94405339
Min length3

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
-3970411.0%
 
4287888.0%
 
B212105.9%
 
9210605.8%
 
8207915.8%
 
6206485.7%
 
F200135.6%
 
0194835.4%
 
D192085.3%
 
C190555.3%
 
5190015.3%
 
E186635.2%
 
1186195.2%
 
A185485.1%
 
2183785.1%
 
3172414.8%
 
7169264.7%
 
n20260.6%
 
a10130.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number20093555.8%
 
Uppercase Letter11669732.4%
 
Dash Punctuation3970411.0%
 
Lowercase Letter30390.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n202666.7%
 
a101333.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
42878814.3%
 
92106010.5%
 
82079110.3%
 
62064810.3%
 
0194839.7%
 
5190019.5%
 
1186199.3%
 
2183789.1%
 
3172418.6%
 
7169268.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B2121018.2%
 
F2001317.1%
 
D1920816.5%
 
C1905516.3%
 
E1866316.0%
 
A1854815.9%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-39704100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common24063966.8%
 
Latin11973633.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
B2121017.7%
 
F2001316.7%
 
D1920816.0%
 
C1905515.9%
 
E1866315.6%
 
A1854815.5%
 
n20261.7%
 
a10130.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
-3970416.5%
 
42878812.0%
 
9210608.8%
 
8207918.6%
 
6206488.6%
 
0194838.1%
 
5190017.9%
 
1186197.7%
 
2183787.6%
 
3172417.2%
 
7169267.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII360375100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
-3970411.0%
 
4287888.0%
 
B212105.9%
 
9210605.8%
 
8207915.8%
 
6206485.7%
 
F200135.6%
 
0194835.4%
 
D192085.3%
 
C190555.3%
 
5190015.3%
 
E186635.2%
 
1186195.2%
 
A185485.1%
 
2183785.1%
 
3172414.8%
 
7169264.7%
 
n20260.6%
 
a10130.3%
 

index1
Real number (ℝ≥0)

UNIQUE

Distinct10939
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5469
Minimum0
Maximum10938
Zeros1
Zeros (%)< 0.1%
Memory size85.6 KiB
2021-04-09T16:44:37.720000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile546.9
Q12734.5
median5469
Q38203.5
95-th percentile10391.1
Maximum10938
Range10938
Interquartile range (IQR)5469

Descriptive statistics

Standard deviation3157.961632
Coefficient of variation (CV)0.5774294444
Kurtosis-1.2
Mean5469
Median Absolute Deviation (MAD)2735
Skewness0
Sum59825391
Variance9972721.667
MonotocityStrictly increasing
2021-04-09T16:44:37.983833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
13141< 0.1%
 
54241< 0.1%
 
95181< 0.1%
 
33711< 0.1%
 
13221< 0.1%
 
74651< 0.1%
 
54161< 0.1%
 
95101< 0.1%
 
33631< 0.1%
 
74571< 0.1%
 
74411< 0.1%
 
54081< 0.1%
 
95021< 0.1%
 
33551< 0.1%
 
13061< 0.1%
 
74491< 0.1%
 
54001< 0.1%
 
94941< 0.1%
 
33471< 0.1%
 
74731< 0.1%
 
13301< 0.1%
 
33791< 0.1%
 
95261< 0.1%
 
75051< 0.1%
 
Other values (10914)1091499.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
109381< 0.1%
 
109371< 0.1%
 
109361< 0.1%
 
109351< 0.1%
 
109341< 0.1%
 
109331< 0.1%
 
109321< 0.1%
 
109311< 0.1%
 
109301< 0.1%
 
109291< 0.1%
 

first_open_time.value
Real number (ℝ≥0)

Distinct324
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.576737656e+12
Minimum1.5112404e+12
Maximum1.6048044e+12
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:38.266670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.5112404e+12
5-th percentile1.539018e+12
Q11.5638868e+12
median1.577268e+12
Q31.5946524e+12
95-th percentile1.6041852e+12
Maximum1.6048044e+12
Range9.3564e+10
Interquartile range (IQR)3.07656e+10

Descriptive statistics

Standard deviation2.016738049e+10
Coefficient of variation (CV)0.01279057452
Kurtosis-0.01284880012
Mean1.576737656e+12
Median Absolute Deviation (MAD)1.42884e+10
Skewness-0.6164188081
Sum1.724793322e+16
Variance4.067232358e+20
MonotocityNot monotonic
2021-04-09T16:44:38.543534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.5638868e+123863.5%
 
1.5874416e+123523.2%
 
1.6041852e+122422.2%
 
1.5464124e+122272.1%
 
1.5507144e+122031.9%
 
1.5671088e+121841.7%
 
1.5689664e+121841.7%
 
1.6004124e+121801.6%
 
1.56204e+121771.6%
 
1.6016004e+121591.5%
 
1.5972588e+121461.3%
 
1.6048044e+121281.2%
 
1.5678288e+121181.1%
 
1.56951e+121171.1%
 
1.588338e+121131.0%
 
1.5841332e+121121.0%
 
1.558152e+121081.0%
 
1.5837444e+121071.0%
 
1.5765408e+121010.9%
 
1.571094e+121010.9%
 
1.5633648e+121000.9%
 
1.5725448e+12950.9%
 
1.6044048e+12950.9%
 
1.584234e+12940.9%
 
1.5608124e+12920.8%
 
Other values (299)701864.2%
 
ValueCountFrequency (%) 
1.5112404e+12330.3%
 
1.5131808e+1290.1%
 
1.5148296e+12130.1%
 
1.51776e+12220.2%
 
1.5179112e+12140.1%
 
1.5181524e+12710.6%
 
1.5289092e+12680.6%
 
1.5321564e+12100.1%
 
1.5322068e+12440.4%
 
1.5334308e+12300.3%
 
ValueCountFrequency (%) 
1.6048044e+121281.2%
 
1.6048008e+12530.5%
 
1.6047936e+1260.1%
 
1.6047792e+12110.1%
 
1.604772e+12150.1%
 
1.604754e+12180.2%
 
1.6047432e+12200.2%
 
1.604718e+12150.1%
 
1.6044048e+12950.9%
 
1.6043652e+12360.3%
 
Distinct337
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size85.6 KiB
Minimum2017-11-21 04:03:35.882000
Maximum2020-11-08 02:48:50.131000
2021-04-09T16:44:38.814375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:39.059828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ga_session_number.value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct340
Distinct (%)3.1%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean271.7818881
Minimum1
Maximum1634
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:39.338830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q160
median144
Q3420
95-th percentile856
Maximum1634
Range1633
Interquartile range (IQR)360

Descriptive statistics

Standard deviation288.6211826
Coefficient of variation (CV)1.061958855
Kurtosis2.122154948
Mean271.7818881
Median Absolute Deviation (MAD)121
Skewness1.477615611
Sum2968130
Variance83302.18704
MonotocityNot monotonic
2021-04-09T16:44:39.633677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1383983.6%
 
1433423.1%
 
113092.8%
 
1272272.1%
 
12081.9%
 
8561971.8%
 
21761.6%
 
681741.6%
 
661671.5%
 
261611.5%
 
1051281.2%
 
1011251.1%
 
6941241.1%
 
81191.1%
 
4201181.1%
 
231151.1%
 
461141.0%
 
251141.0%
 
51000.9%
 
160960.9%
 
535950.9%
 
494950.9%
 
7860.8%
 
100850.8%
 
65840.8%
 
Other values (315)696463.7%
 
ValueCountFrequency (%) 
12081.9%
 
21761.6%
 
3280.3%
 
4100.1%
 
51000.9%
 
6410.4%
 
7860.8%
 
81191.1%
 
9160.1%
 
10320.3%
 
ValueCountFrequency (%) 
1634320.3%
 
163390.1%
 
1421180.2%
 
1158380.3%
 
1141100.1%
 
1123200.2%
 
1122680.6%
 
105590.1%
 
10545< 0.1%
 
9745< 0.1%
 
Distinct522
Distinct (%)4.8%
Missing18
Missing (%)0.2%
Memory size85.6 KiB
Minimum2020-08-09 14:20:42.273000
Maximum2020-11-08 06:56:18.525000
2021-04-09T16:44:39.949479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:40.249310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ga_session_id.value
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct519
Distinct (%)4.8%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1604778991
Minimum1596982842
Maximum1604818577
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:40.579142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1596982842
5-th percentile1604739154
Q11604758627
median1604786128
Q31604800899
95-th percentile1604809036
Maximum1604818577
Range7835735
Interquartile range (IQR)42272

Descriptive statistics

Standard deviation78361.9965
Coefficient of variation (CV)4.883039778e-05
Kurtosis8973.670615
Mean1604778991
Median Absolute Deviation (MAD)18625
Skewness-90.20279982
Sum1.752579136e+13
Variance6140602495
MonotocityNot monotonic
2021-04-09T16:44:40.856365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16047470573863.5%
 
16047472313423.1%
 
16047737222422.2%
 
16047939302272.1%
 
16047906551951.8%
 
16047936531591.5%
 
16047438061251.1%
 
16047465661241.1%
 
16047931891071.0%
 
1604739154950.9%
 
1604796661950.9%
 
1604794822940.9%
 
1604804257920.8%
 
1604780599840.8%
 
1604800893810.7%
 
1604799285780.7%
 
1604795808750.7%
 
1604804753730.7%
 
1604798369710.6%
 
1604808410700.6%
 
1604763359690.6%
 
1604802873690.6%
 
1604804746680.6%
 
1604785413680.6%
 
1604810812680.6%
 
Other values (494)776471.0%
 
ValueCountFrequency (%) 
15969828421< 0.1%
 
16047324892< 0.1%
 
1604733194130.1%
 
1604733413190.2%
 
1604733418190.2%
 
1604733482120.1%
 
16047335482< 0.1%
 
1604733568420.4%
 
16047340294< 0.1%
 
1604735193280.3%
 
ValueCountFrequency (%) 
160481857780.1%
 
160481841570.1%
 
160481702160.1%
 
160481672890.1%
 
160481659680.1%
 
1604816417310.3%
 
1604816235440.4%
 
160481615790.1%
 
1604815786350.3%
 
1604815681200.2%
 
Distinct522
Distinct (%)4.8%
Missing18
Missing (%)0.2%
Memory size85.6 KiB
Minimum2020-08-09 14:20:42.273000
Maximum2020-11-08 06:56:17.204000
2021-04-09T16:44:41.121992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:41.426508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
auto
9163 
app
1776 
ValueCountFrequency (%) 
auto916383.8%
 
app177616.2%
 
2021-04-09T16:44:41.742327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:41.901258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:42.065160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.837645123
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41980100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin41980100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII41980100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
auto
9163 
app
1776 
ValueCountFrequency (%) 
auto916383.8%
 
app177616.2%
 
2021-04-09T16:44:42.299009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:42.461919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:42.626039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.837645123
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41980100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin41980100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII41980100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a1093926.1%
 
u916321.8%
 
t916321.8%
 
o916321.8%
 
p35528.5%
 

ga_session_id
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct519
Distinct (%)4.8%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1604778991
Minimum1596982842
Maximum1604818577
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:42.856036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1596982842
5-th percentile1604739154
Q11604758627
median1604786128
Q31604800899
95-th percentile1604809036
Maximum1604818577
Range7835735
Interquartile range (IQR)42272

Descriptive statistics

Standard deviation78361.9965
Coefficient of variation (CV)4.883039778e-05
Kurtosis8973.670615
Mean1604778991
Median Absolute Deviation (MAD)18625
Skewness-90.20279982
Sum1.752579136e+13
Variance6140602495
MonotocityNot monotonic
2021-04-09T16:44:43.106023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
16047470573863.5%
 
16047472313423.1%
 
16047737222422.2%
 
16047939302272.1%
 
16047906551951.8%
 
16047936531591.5%
 
16047438061251.1%
 
16047465661241.1%
 
16047931891071.0%
 
1604739154950.9%
 
1604796661950.9%
 
1604794822940.9%
 
1604804257920.8%
 
1604780599840.8%
 
1604800893810.7%
 
1604799285780.7%
 
1604795808750.7%
 
1604804753730.7%
 
1604798369710.6%
 
1604808410700.6%
 
1604763359690.6%
 
1604802873690.6%
 
1604804746680.6%
 
1604785413680.6%
 
1604810812680.6%
 
Other values (494)776471.0%
 
ValueCountFrequency (%) 
15969828421< 0.1%
 
16047324892< 0.1%
 
1604733194130.1%
 
1604733413190.2%
 
1604733418190.2%
 
1604733482120.1%
 
16047335482< 0.1%
 
1604733568420.4%
 
16047340294< 0.1%
 
1604735193280.3%
 
ValueCountFrequency (%) 
160481857780.1%
 
160481841570.1%
 
160481702160.1%
 
160481672890.1%
 
160481659680.1%
 
1604816417310.3%
 
1604816235440.4%
 
160481615790.1%
 
1604815786350.3%
 
1604815681200.2%
 

engaged_session_event
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing294
Missing (%)2.7%
Memory size85.6 KiB
1
10645 
(Missing)
 
294
ValueCountFrequency (%) 
11064597.3%
 
(Missing)2942.7%
 
2021-04-09T16:44:43.343192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

session_engaged
Boolean

MISSING

Distinct1
Distinct (%)0.2%
Missing10487
Missing (%)95.9%
Memory size85.6 KiB
1
 
452
(Missing)
10487 
ValueCountFrequency (%) 
14524.1%
 
(Missing)1048795.9%
 
2021-04-09T16:44:43.412153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

ga_session_number
Real number (ℝ≥0)

HIGH CORRELATION

Distinct340
Distinct (%)3.1%
Missing18
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean271.7818881
Minimum1
Maximum1634
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:43.580056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q160
median144
Q3420
95-th percentile856
Maximum1634
Range1633
Interquartile range (IQR)360

Descriptive statistics

Standard deviation288.6211826
Coefficient of variation (CV)1.061958855
Kurtosis2.122154948
Mean271.7818881
Median Absolute Deviation (MAD)121
Skewness1.477615611
Sum2968130
Variance83302.18704
MonotocityNot monotonic
2021-04-09T16:44:43.864893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1383983.6%
 
1433423.1%
 
113092.8%
 
1272272.1%
 
12081.9%
 
8561971.8%
 
21761.6%
 
681741.6%
 
661671.5%
 
261611.5%
 
1051281.2%
 
1011251.1%
 
6941241.1%
 
81191.1%
 
4201181.1%
 
231151.1%
 
461141.0%
 
251141.0%
 
51000.9%
 
160960.9%
 
535950.9%
 
494950.9%
 
7860.8%
 
100850.8%
 
65840.8%
 
Other values (315)696463.7%
 
ValueCountFrequency (%) 
12081.9%
 
21761.6%
 
3280.3%
 
4100.1%
 
51000.9%
 
6410.4%
 
7860.8%
 
81191.1%
 
9160.1%
 
10320.3%
 
ValueCountFrequency (%) 
1634320.3%
 
163390.1%
 
1421180.2%
 
1158380.3%
 
1141100.1%
 
1123200.2%
 
1122680.6%
 
105590.1%
 
10545< 0.1%
 
9745< 0.1%
 

firebase_screen_id
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct486
Distinct (%)4.6%
Missing453
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean4.368961194e+17
Minimum-9.135996112e+18
Maximum9.171393132e+18
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:44.182711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-9.135996112e+18
5-th percentile-8.117814687e+18
Q1-4.120004969e+18
median4.198573706e+17
Q35.78411972e+18
95-th percentile8.490210533e+18
Maximum9.171393132e+18
Range1.830738924e+19
Interquartile range (IQR)9.90412469e+18

Descriptive statistics

Standard deviation5.390224719e+18
Coefficient of variation (CV)12.33754313
Kurtosis-1.193671058
Mean4.368961194e+17
Median Absolute Deviation (MAD)4.712522675e+18
Skewness-0.02493001422
Sum4.581292708e+21
Variance2.905452252e+37
MonotocityNot monotonic
2021-04-09T16:44:44.481559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.644756432e+183853.5%
 
8.490210533e+182892.6%
 
6.576742543e+181941.8%
 
6.287030342e+171801.6%
 
-2.164389897e+181341.2%
 
-8.905193006e+181231.1%
 
-9.045768455e+171121.0%
 
-8.383647882e+17990.9%
 
-6.691526507e+18980.9%
 
7.921229761e+18970.9%
 
-4.292665304e+18930.9%
 
7.046984218e+18830.8%
 
9.068051895e+18830.8%
 
-5.29380703e+18820.7%
 
-6.535752208e+18800.7%
 
7.112106142e+17770.7%
 
4.340681327e+18760.7%
 
5.78411972e+18750.7%
 
4.158304643e+18710.6%
 
-7.628228153e+18710.6%
 
-6.728687376e+18700.6%
 
9.171393132e+18690.6%
 
7.084446322e+18680.6%
 
-8.70966911e+18670.6%
 
3.141928498e+18670.6%
 
Other values (461)764369.9%
 
(Missing)4534.1%
 
ValueCountFrequency (%) 
-9.135996112e+182< 0.1%
 
-9.126597265e+18140.1%
 
-8.97335264e+1890.1%
 
-8.953136172e+1870.1%
 
-8.941271829e+1870.1%
 
-8.905193006e+181231.1%
 
-8.901127173e+18220.2%
 
-8.860113247e+1880.1%
 
-8.730696942e+18140.1%
 
-8.70966911e+18670.6%
 
ValueCountFrequency (%) 
9.171393132e+18690.6%
 
9.128259134e+18180.2%
 
9.096729108e+18280.3%
 
9.068051895e+18830.8%
 
9.031297883e+183< 0.1%
 
9.0037733e+18150.1%
 
8.924371813e+18350.3%
 
8.869141346e+1890.1%
 
8.858243216e+1870.1%
 
8.847725755e+183< 0.1%
 

firebase_screen_class
Categorical

MISSING

Distinct21
Distinct (%)0.2%
Missing453
Missing (%)4.1%
Memory size11.6 KiB
HymnListViewController
3939 
HymnViewController
3886 
UIAlertController
570 
StyledPreviewController
517 
CenterViewController
479 
Other values (16)
1095 
ValueCountFrequency (%) 
HymnListViewController393936.0%
 
HymnViewController388635.5%
 
UIAlertController5705.2%
 
StyledPreviewController5174.7%
 
CenterViewController4794.4%
 
MainActivity4073.7%
 
ViewerActivity3283.0%
 
MDCBottomSheetController2142.0%
 
UIActivityContentViewController290.3%
 
UpdateViewController290.3%
 
SongbookAlertController260.2%
 
SLComposeViewController170.2%
 
SubmissionsViewController80.1%
 
OrganizeViewController70.1%
 
UIActivityViewController70.1%
 
ContainerViewController70.1%
 
ReviewViewController60.1%
 
MeetViewController4< 0.1%
 
SongbookViewController2< 0.1%
 
ComposeViewController2< 0.1%
 
SFAirDropViewController2< 0.1%
 
(Missing)4534.1%
 
2021-04-09T16:44:44.811352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:45.043162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length18
Mean length18.94259073
Min length3

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e2247510.8%
 
r2144010.3%
 
l206159.9%
 
o200989.7%
 
n194839.4%
 
t175648.5%
 
i151957.3%
 
C104995.1%
 
w92754.5%
 
y91134.4%
 
V87524.2%
 
m80663.9%
 
H78253.8%
 
s39821.9%
 
L39561.9%
 
A13690.7%
 
v12940.6%
 
a9030.4%
 
S7860.4%
 
c7710.4%
 
U6350.3%
 
M6250.3%
 
I6060.3%
 
d5460.3%
 
P5170.2%
 
Other values (12)8230.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter17119882.6%
 
Uppercase Letter3601517.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e2247513.1%
 
r2144012.5%
 
l2061512.0%
 
o2009811.7%
 
n1948311.4%
 
t1756410.3%
 
i151958.9%
 
w92755.4%
 
y91135.3%
 
m80664.7%
 
s39822.3%
 
v12940.8%
 
a9030.5%
 
c7710.5%
 
d5460.3%
 
h2140.1%
 
p50< 0.1%
 
b36< 0.1%
 
g35< 0.1%
 
k28< 0.1%
 
u8< 0.1%
 
z7< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1049929.2%
 
V875224.3%
 
H782521.7%
 
L395611.0%
 
A13693.8%
 
S7862.2%
 
U6351.8%
 
M6251.7%
 
I6061.7%
 
P5171.4%
 
D2160.6%
 
B2140.6%
 
O7< 0.1%
 
R6< 0.1%
 
F2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin207213100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e2247510.8%
 
r2144010.3%
 
l206159.9%
 
o200989.7%
 
n194839.4%
 
t175648.5%
 
i151957.3%
 
C104995.1%
 
w92754.5%
 
y91134.4%
 
V87524.2%
 
m80663.9%
 
H78253.8%
 
s39821.9%
 
L39561.9%
 
A13690.7%
 
v12940.6%
 
a9030.4%
 
S7860.4%
 
c7710.4%
 
U6350.3%
 
M6250.3%
 
I6060.3%
 
d5460.3%
 
P5170.2%
 
Other values (12)8230.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII207213100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e2247510.8%
 
r2144010.3%
 
l206159.9%
 
o200989.7%
 
n194839.4%
 
t175648.5%
 
i151957.3%
 
C104995.1%
 
w92754.5%
 
y91134.4%
 
V87524.2%
 
m80663.9%
 
H78253.8%
 
s39821.9%
 
L39561.9%
 
A13690.7%
 
v12940.6%
 
a9030.4%
 
S7860.4%
 
c7710.4%
 
U6350.3%
 
M6250.3%
 
I6060.3%
 
d5460.3%
 
P5170.2%
 
Other values (12)8230.4%
 

engagement_time_msec
Real number (ℝ≥0)

MISSING

Distinct3905
Distinct (%)83.0%
Missing6232
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean62136.5188
Minimum0
Maximum3600067
Zeros13
Zeros (%)0.1%
Memory size85.6 KiB
2021-04-09T16:44:45.286887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile403.8
Q12002
median6600
Q327263.5
95-th percentile296219.7
Maximum3600067
Range3600067
Interquartile range (IQR)25261.5

Descriptive statistics

Standard deviation230043.8897
Coefficient of variation (CV)3.702233311
Kurtosis156.6879733
Mean62136.5188
Median Absolute Deviation (MAD)5342
Skewness11.11069229
Sum292476594
Variance5.29201912e+10
MonotocityNot monotonic
2021-04-09T16:44:45.575744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1560.5%
 
2180.2%
 
3160.1%
 
0130.1%
 
165960.1%
 
11785< 0.1%
 
14405< 0.1%
 
20335< 0.1%
 
17765< 0.1%
 
46994< 0.1%
 
10534< 0.1%
 
11014< 0.1%
 
10954< 0.1%
 
48104< 0.1%
 
44744< 0.1%
 
15474< 0.1%
 
11414< 0.1%
 
10244< 0.1%
 
14784< 0.1%
 
13694< 0.1%
 
10293< 0.1%
 
17683< 0.1%
 
27213< 0.1%
 
10883< 0.1%
 
43113< 0.1%
 
Other values (3880)451941.3%
 
(Missing)623257.0%
 
ValueCountFrequency (%) 
0130.1%
 
1560.5%
 
2180.2%
 
3160.1%
 
42< 0.1%
 
51< 0.1%
 
63< 0.1%
 
72< 0.1%
 
91< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
36000671< 0.1%
 
36000661< 0.1%
 
36000652< 0.1%
 
36000631< 0.1%
 
36000621< 0.1%
 
36000532< 0.1%
 
36000512< 0.1%
 
36000481< 0.1%
 
36000011< 0.1%
 
34357021< 0.1%
 

entrances
Boolean

MISSING

Distinct1
Distinct (%)0.2%
Missing10466
Missing (%)95.7%
Memory size85.6 KiB
1
 
473
(Missing)
10466 
ValueCountFrequency (%) 
14734.3%
 
(Missing)1046695.7%
 
2021-04-09T16:44:45.796793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

freeride
Boolean

MISSING

Distinct1
Distinct (%)0.5%
Missing10727
Missing (%)98.1%
Memory size85.6 KiB
1
 
212
(Missing)
10727 
ValueCountFrequency (%) 
12121.9%
 
(Missing)1072798.1%
 
2021-04-09T16:44:45.862776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

firebase_previous_id
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct453
Distinct (%)12.5%
Missing7310
Missing (%)66.8%
Infinite0
Infinite (%)0.0%
Mean9.013993022e+17
Minimum-9.126597265e+18
Maximum9.171393132e+18
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:46.029675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-9.126597265e+18
5-th percentile-7.944074368e+18
Q1-3.656869028e+18
median6.287030342e+17
Q36.576742543e+18
95-th percentile8.490210533e+18
Maximum9.171393132e+18
Range1.82979904e+19
Interquartile range (IQR)1.023361157e+19

Descriptive statistics

Standard deviation5.389722795e+18
Coefficient of variation (CV)5.97928441
Kurtosis-1.197618332
Mean9.013993022e+17
Median Absolute Deviation (MAD)5.10469052e+18
Skewness-0.1313118799
Sum3.271178068e+21
Variance2.904911181e+37
MonotocityNot monotonic
2021-04-09T16:44:46.315495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.644756432e+181601.5%
 
8.490210533e+181521.4%
 
6.576742543e+181141.0%
 
6.287030342e+17720.7%
 
-2.164389897e+18460.4%
 
-8.383647882e+17450.4%
 
7.921229761e+18450.4%
 
-9.045768455e+17380.3%
 
-6.691526507e+18380.3%
 
5.78411972e+18350.3%
 
7.046984218e+18350.3%
 
8.490210533e+18340.3%
 
-4.292665304e+18340.3%
 
-6.535752208e+18330.3%
 
-6.728687376e+18320.3%
 
4.158304643e+18310.3%
 
4.539133181e+17310.3%
 
4.143379054e+18300.3%
 
7.112106142e+17290.3%
 
3.350095794e+18290.3%
 
9.171393132e+18280.3%
 
3.141928498e+18280.3%
 
-8.905193006e+18270.2%
 
-3.656869028e+18270.2%
 
7.084446322e+18270.2%
 
Other values (428)242922.2%
 
(Missing)731066.8%
 
ValueCountFrequency (%) 
-9.126597265e+184< 0.1%
 
-8.97335264e+183< 0.1%
 
-8.953136172e+182< 0.1%
 
-8.941271829e+182< 0.1%
 
-8.905193006e+18270.2%
 
-8.901127173e+1890.1%
 
-8.860113247e+182< 0.1%
 
-8.730696942e+184< 0.1%
 
-8.70966911e+18200.2%
 
-8.686771845e+18100.1%
 
ValueCountFrequency (%) 
9.171393132e+18280.3%
 
9.128259134e+184< 0.1%
 
9.096729108e+1890.1%
 
9.068051895e+18250.2%
 
9.031297883e+181< 0.1%
 
9.0037733e+185< 0.1%
 
8.924371813e+18120.1%
 
8.869141346e+184< 0.1%
 
8.858243216e+181< 0.1%
 
8.847725755e+181< 0.1%
 

firebase_previous_class
Categorical

MISSING

Distinct21
Distinct (%)0.6%
Missing7310
Missing (%)66.8%
Memory size11.6 KiB
HymnViewController
1318 
HymnListViewController
963 
CenterViewController
458 
UIAlertController
325 
StyledPreviewController
166 
Other values (16)
399 
ValueCountFrequency (%) 
HymnViewController131812.0%
 
HymnListViewController9638.8%
 
CenterViewController4584.2%
 
UIAlertController3253.0%
 
StyledPreviewController1661.5%
 
MainActivity1151.1%
 
MDCBottomSheetController1041.0%
 
ViewerActivity1010.9%
 
UIActivityContentViewController170.2%
 
SongbookAlertController140.1%
 
UpdateViewController110.1%
 
SLComposeViewController80.1%
 
UIActivityViewController70.1%
 
ContainerViewController60.1%
 
OrganizeViewController4< 0.1%
 
SubmissionsViewController4< 0.1%
 
ReviewViewController3< 0.1%
 
MeetViewController2< 0.1%
 
SongbookViewController1< 0.1%
 
ComposeViewController1< 0.1%
 
SFAirDropViewController1< 0.1%
 
(Missing)731066.8%
 
2021-04-09T16:44:46.659299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.1%
2021-04-09T16:44:46.902235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length31
Median length3
Mean length8.459822653
Min length3

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2095622.6%
 
e84379.1%
 
r79028.5%
 
a74468.0%
 
l73317.9%
 
o71257.7%
 
t61846.7%
 
i46515.0%
 
C40074.3%
 
w30743.3%
 
V29053.1%
 
y26872.9%
 
m23982.6%
 
H22812.5%
 
s9841.1%
 
L9711.0%
 
A5800.6%
 
v4090.4%
 
U3600.4%
 
I3490.4%
 
S2980.3%
 
c2400.3%
 
M2210.2%
 
d1770.2%
 
P1660.2%
 
Other values (12)4030.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter8018786.6%
 
Uppercase Letter1235513.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2095626.1%
 
e843710.5%
 
r79029.9%
 
a74469.3%
 
l73319.1%
 
o71258.9%
 
t61847.7%
 
i46515.8%
 
w30743.8%
 
y26873.4%
 
m23983.0%
 
s9841.2%
 
v4090.5%
 
c2400.3%
 
d1770.2%
 
h1040.1%
 
p21< 0.1%
 
g19< 0.1%
 
b19< 0.1%
 
k15< 0.1%
 
z4< 0.1%
 
u4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C400732.4%
 
V290523.5%
 
H228118.5%
 
L9717.9%
 
A5804.7%
 
U3602.9%
 
I3492.8%
 
S2982.4%
 
M2211.8%
 
P1661.3%
 
D1050.8%
 
B1040.8%
 
O4< 0.1%
 
R3< 0.1%
 
F1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin92542100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2095622.6%
 
e84379.1%
 
r79028.5%
 
a74468.0%
 
l73317.9%
 
o71257.7%
 
t61846.7%
 
i46515.0%
 
C40074.3%
 
w30743.3%
 
V29053.1%
 
y26872.9%
 
m23982.6%
 
H22812.5%
 
s9841.1%
 
L9711.0%
 
A5800.6%
 
v4090.4%
 
U3600.4%
 
I3490.4%
 
S2980.3%
 
c2400.3%
 
M2210.2%
 
d1770.2%
 
P1660.2%
 
Other values (12)4030.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII92542100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2095622.6%
 
e84379.1%
 
r79028.5%
 
a74468.0%
 
l73317.9%
 
o71257.7%
 
t61846.7%
 
i46515.0%
 
C40074.3%
 
w30743.3%
 
V29053.1%
 
y26872.9%
 
m23982.6%
 
H22812.5%
 
s9841.1%
 
L9711.0%
 
A5800.6%
 
v4090.4%
 
U3600.4%
 
I3490.4%
 
S2980.3%
 
c2400.3%
 
M2210.2%
 
d1770.2%
 
P1660.2%
 
Other values (12)4030.4%
 

item_subcategory
Categorical

HIGH CARDINALITY
MISSING

Distinct196
Distinct (%)21.7%
Missing10035
Missing (%)91.7%
Memory size28.0 KiB
Loving Him
 
45
Remembrance of Him
 
43
General
 
32
His Love
 
31
His Redemption
 
25
Other values (191)
728 
ValueCountFrequency (%) 
Loving Him450.4%
 
Remembrance of Him430.4%
 
General320.3%
 
His Love310.3%
 
His Redemption250.2%
 
His Plan230.2%
 
As Everything200.2%
 
As Life190.2%
 
Constrained by the Lord's Love180.2%
 
Ministering Christ170.2%
 
As the Son of Man on the Throne150.1%
 
His Beauty150.1%
 
Surrendering All to the Lord140.1%
 
Satisfaction with Him140.1%
 
His Exaltation130.1%
 
Life in Eternity130.1%
 
For Growth in Christ120.1%
 
The Lord's Recovery110.1%
 
Freedom110.1%
 
Satisfied with Christ100.1%
 
His Name100.1%
 
His All-Inclusiveness100.1%
 
As the Source of Life100.1%
 
For Fellowship with Christ90.1%
 
His Incarnation90.1%
 
Other values (171)4554.2%
 
(Missing)1003591.7%
 
2021-04-09T16:44:47.176611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique66 ?
Unique (%)7.3%
2021-04-09T16:44:47.490430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length42
Median length3
Mean length4.140323613
Min length3

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2098946.3%
 
a1050323.2%
 
18664.1%
 
i13823.1%
 
e13543.0%
 
o9762.2%
 
r9392.1%
 
t8771.9%
 
s8661.9%
 
h6131.4%
 
d3960.9%
 
H3870.9%
 
L3460.8%
 
l3250.7%
 
m3080.7%
 
g2750.6%
 
f2590.6%
 
v2220.5%
 
y2160.5%
 
c1940.4%
 
C1790.4%
 
A1750.4%
 
F1420.3%
 
u1390.3%
 
R1340.3%
 
Other values (26)12292.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4122491.0%
 
Uppercase Letter21004.6%
 
Space Separator18664.1%
 
Other Punctuation790.2%
 
Final Punctuation12< 0.1%
 
Dash Punctuation10< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2098950.9%
 
a1050325.5%
 
i13823.4%
 
e13543.3%
 
o9762.4%
 
r9392.3%
 
t8772.1%
 
s8662.1%
 
h6131.5%
 
d3961.0%
 
l3250.8%
 
m3080.7%
 
g2750.7%
 
f2590.6%
 
v2220.5%
 
y2160.5%
 
c1940.5%
 
u1390.3%
 
w1330.3%
 
b1050.3%
 
p1040.3%
 
k270.1%
 
x19< 0.1%
 
j3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H38718.4%
 
L34616.5%
 
C1798.5%
 
A1758.3%
 
F1426.8%
 
R1346.4%
 
S1336.3%
 
G894.2%
 
B803.8%
 
P733.5%
 
T643.0%
 
E633.0%
 
M562.7%
 
I442.1%
 
D311.5%
 
W311.5%
 
N271.3%
 
V201.0%
 
O170.8%
 
J40.2%
 
Y40.2%
 
K1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1866100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'7594.9%
 
,45.1%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
12100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-10100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4332495.7%
 
Common19674.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2098948.4%
 
a1050324.2%
 
i13823.2%
 
e13543.1%
 
o9762.3%
 
r9392.2%
 
t8772.0%
 
s8662.0%
 
h6131.4%
 
d3960.9%
 
H3870.9%
 
L3460.8%
 
l3250.8%
 
m3080.7%
 
g2750.6%
 
f2590.6%
 
v2220.5%
 
y2160.5%
 
c1940.4%
 
C1790.4%
 
A1750.4%
 
F1420.3%
 
u1390.3%
 
R1340.3%
 
w1330.3%
 
Other values (21)9952.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
186694.9%
 
'753.8%
 
120.6%
 
-100.5%
 
,40.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII45279> 99.9%
 
Punctuation12< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2098946.4%
 
a1050323.2%
 
18664.1%
 
i13823.1%
 
e13543.0%
 
o9762.2%
 
r9392.1%
 
t8771.9%
 
s8661.9%
 
h6131.4%
 
d3960.9%
 
H3870.9%
 
L3460.8%
 
l3250.7%
 
m3080.7%
 
g2750.6%
 
f2590.6%
 
v2220.5%
 
y2160.5%
 
c1940.4%
 
C1790.4%
 
A1750.4%
 
F1420.3%
 
u1390.3%
 
R1340.3%
 
Other values (25)12172.7%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
12100.0%
 

recommended
Boolean

MISSING

Distinct2
Distinct (%)0.2%
Missing9965
Missing (%)91.1%
Memory size85.6 KiB
0
 
971
1
 
3
(Missing)
9965 
ValueCountFrequency (%) 
09718.9%
 
13< 0.1%
 
(Missing)996591.1%
 
2021-04-09T16:44:47.684318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

item_category
Categorical

MISSING

Distinct26
Distinct (%)2.7%
Missing9988
Missing (%)91.3%
Memory size12.3 KiB
Praise of the Lord
188 
Experience of Christ
145 
Consecration
82 
Worship of the Father
76 
Longings
62 
Other values (21)
398 
ValueCountFrequency (%) 
Praise of the Lord1881.7%
 
Experience of Christ1451.3%
 
Consecration820.7%
 
Worship of the Father760.7%
 
Longings620.6%
 
The Church510.5%
 
Gospel500.5%
 
Scriptures for Singing470.4%
 
Assurance and Joy of Salvation390.4%
 
Blessing of the Trinity340.3%
 
Service300.3%
 
Hope of Glory220.2%
 
Fulness of the Spirit200.2%
 
Encouragement190.2%
 
Ultimate Manifestation170.2%
 
Comfort in Trials160.1%
 
Experience of God90.1%
 
Spiritual Warfare80.1%
 
Union with Christ80.1%
 
Study of the Word70.1%
 
Prayer70.1%
 
Preaching of the Gospel4< 0.1%
 
The Kingdom3< 0.1%
 
Various Aspects of the Inner Life3< 0.1%
 
The Word of God2< 0.1%
 
(Missing)998891.3%
 
2021-04-09T16:44:47.891200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:48.158048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length33
Median length3
Mean length4.185483134
Min length3

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2083645.5%
 
a1061423.2%
 
17373.8%
 
e15763.4%
 
o13643.0%
 
r13623.0%
 
i11972.6%
 
t9142.0%
 
s8921.9%
 
h8071.8%
 
f6401.4%
 
c4290.9%
 
p3840.8%
 
C3020.7%
 
g2800.6%
 
d2570.6%
 
L2530.6%
 
l2100.5%
 
P1990.4%
 
S1980.4%
 
u1940.4%
 
E1730.4%
 
x1540.3%
 
y1090.2%
 
T1060.2%
 
Other values (15)5981.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4235192.5%
 
Space Separator17373.8%
 
Uppercase Letter16973.7%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2083649.2%
 
a1061425.1%
 
e15763.7%
 
o13643.2%
 
r13623.2%
 
i11972.8%
 
t9142.2%
 
s8922.1%
 
h8071.9%
 
f6401.5%
 
c4291.0%
 
p3840.9%
 
g2800.7%
 
d2570.6%
 
l2100.5%
 
u1940.5%
 
x1540.4%
 
y1090.3%
 
v690.2%
 
m550.1%
 
w8< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C30217.8%
 
L25314.9%
 
P19911.7%
 
S19811.7%
 
E17310.2%
 
T1066.2%
 
F965.7%
 
W935.5%
 
G875.1%
 
A422.5%
 
J392.3%
 
B342.0%
 
U251.5%
 
H221.3%
 
M191.1%
 
V30.2%
 
I30.2%
 
K30.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1737100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4404896.2%
 
Common17373.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2083647.3%
 
a1061424.1%
 
e15763.6%
 
o13643.1%
 
r13623.1%
 
i11972.7%
 
t9142.1%
 
s8922.0%
 
h8071.8%
 
f6401.5%
 
c4291.0%
 
p3840.9%
 
C3020.7%
 
g2800.6%
 
d2570.6%
 
L2530.6%
 
l2100.5%
 
P1990.5%
 
S1980.4%
 
u1940.4%
 
E1730.4%
 
x1540.3%
 
y1090.2%
 
T1060.2%
 
F960.2%
 
Other values (14)5021.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
1737100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII45785100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2083645.5%
 
a1061423.2%
 
17373.8%
 
e15763.4%
 
o13643.0%
 
r13623.0%
 
i11972.6%
 
t9142.0%
 
s8921.9%
 
h8071.8%
 
f6401.4%
 
c4290.9%
 
p3840.8%
 
C3020.7%
 
g2800.6%
 
d2570.6%
 
L2530.6%
 
l2100.5%
 
P1990.4%
 
S1980.4%
 
u1940.4%
 
E1730.4%
 
x1540.3%
 
y1090.2%
 
T1060.2%
 
Other values (15)5981.3%
 

item_name
Categorical

HIGH CARDINALITY
MISSING

Distinct480
Distinct (%)49.3%
Missing9965
Missing (%)91.1%
Memory size45.2 KiB
Glory be to God the Father
 
23
Christ to minister is service
 
17
Lord, thank You for a new day
 
14
River of living water
 
13
O God, Thou art the source of life
 
10
Other values (475)
897 
ValueCountFrequency (%) 
Glory be to God the Father230.2%
 
Christ to minister is service170.2%
 
Lord, thank You for a new day 140.1%
 
River of living water130.1%
 
O God, Thou art the source of life100.1%
 
Once it was the blessing100.1%
 
Pursue Him and know Him100.1%
 
All my life long I had panted100.1%
 
Faith is for appreciating, substantiating90.1%
 
To Jerusalem we've come90.1%
 
In the bosom of the Father80.1%
 
Dearest Lord, You've called us here80.1%
 
What caused Mary to give her all?80.1%
 
It passeth knowledge, that dear love of Thine80.1%
 
Hidden behind every scene70.1%
 
Sweet feast of love divine!70.1%
 
Serve and work within the Body70.1%
 
Myst'ry hid from ages now revealed to me70.1%
 
Jesus Lord, truly I love Thee70.1%
 
I cannot breathe enough of Thee70.1%
 
We're gathered here, O Lord, as Thy one Body:60.1%
 
For the bread and for the wine60.1%
 
To God be the glory, great things He hath done60.1%
 
Fill all my vision, Savior, I pray60.1%
 
Lo! in heaven Jesus sitting60.1%
 
Other values (455)7456.8%
 
(Missing)996591.1%
 
2021-04-09T16:44:48.434912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique282 ?
Unique (%)29.0%
2021-04-09T16:44:48.730722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length95
Median length3
Mean length5.600786178
Min length3

Overview of Unicode Properties

Unique unicode characters63
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2121734.6%
 
a1154518.8%
 
53828.8%
 
e31285.1%
 
o22303.6%
 
t17732.9%
 
r16972.8%
 
h15142.5%
 
s15032.5%
 
i14322.3%
 
d10241.7%
 
l9221.5%
 
u7651.2%
 
y5911.0%
 
,5760.9%
 
m5510.9%
 
w5120.8%
 
g4850.8%
 
v4600.8%
 
f4580.7%
 
c3080.5%
 
T3060.5%
 
b2750.4%
 
p2690.4%
 
L2430.4%
 
Other values (38)21013.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5282986.2%
 
Space Separator53828.8%
 
Uppercase Letter21303.5%
 
Other Punctuation8971.5%
 
Dash Punctuation19< 0.1%
 
Final Punctuation7< 0.1%
 
Decimal Number3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2121740.2%
 
a1154521.9%
 
e31285.9%
 
o22304.2%
 
t17733.4%
 
r16973.2%
 
h15142.9%
 
s15032.8%
 
i14322.7%
 
d10241.9%
 
l9221.7%
 
u7651.4%
 
y5911.1%
 
m5511.0%
 
w5121.0%
 
g4850.9%
 
v4600.9%
 
f4580.9%
 
c3080.6%
 
b2750.5%
 
p2690.5%
 
k1330.3%
 
j24< 0.1%
 
x5< 0.1%
 
z5< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T30614.4%
 
L24311.4%
 
I2069.7%
 
G1888.8%
 
O1476.9%
 
H1406.6%
 
F1276.0%
 
J1276.0%
 
C1014.7%
 
S1004.7%
 
W954.5%
 
Y884.1%
 
M472.2%
 
B472.2%
 
A381.8%
 
D371.7%
 
R291.4%
 
P200.9%
 
N200.9%
 
K120.6%
 
E70.3%
 
V40.2%
 
U1< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
5382100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,57664.2%
 
'14215.8%
 
!10812.0%
 
?232.6%
 
:182.0%
 
.171.9%
 
"80.9%
 
;50.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
1789.5%
 
-210.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1266.7%
 
9133.3%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
7100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5495989.7%
 
Common630810.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2121738.6%
 
a1154521.0%
 
e31285.7%
 
o22304.1%
 
t17733.2%
 
r16973.1%
 
h15142.8%
 
s15032.7%
 
i14322.6%
 
d10241.9%
 
l9221.7%
 
u7651.4%
 
y5911.1%
 
m5511.0%
 
w5120.9%
 
g4850.9%
 
v4600.8%
 
f4580.8%
 
c3080.6%
 
T3060.6%
 
b2750.5%
 
p2690.5%
 
L2430.4%
 
I2060.4%
 
G1880.3%
 
Other values (24)13572.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
538285.3%
 
,5769.1%
 
'1422.3%
 
!1081.7%
 
?230.4%
 
:180.3%
 
.170.3%
 
170.3%
 
"80.1%
 
70.1%
 
;50.1%
 
12< 0.1%
 
-2< 0.1%
 
91< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII61243> 99.9%
 
Punctuation24< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2121734.6%
 
a1154518.9%
 
53828.8%
 
e31285.1%
 
o22303.6%
 
t17732.9%
 
r16972.8%
 
h15142.5%
 
s15032.5%
 
i14322.3%
 
d10241.7%
 
l9221.5%
 
u7651.2%
 
y5911.0%
 
,5760.9%
 
m5510.9%
 
w5120.8%
 
g4850.8%
 
v4600.8%
 
f4580.7%
 
c3080.5%
 
T3060.5%
 
b2750.4%
 
p2690.4%
 
L2430.4%
 
Other values (36)20773.4%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
1770.8%
 
729.2%
 

item_number
Real number (ℝ)

MISSING

Distinct472
Distinct (%)48.5%
Missing9965
Missing (%)91.1%
Infinite0
Infinite (%)0.0%
Mean963.4794661
Minimum-1
Maximum8773
Zeros0
Zeros (%)0.0%
Memory size85.6 KiB
2021-04-09T16:44:49.940826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile14.95
Q1224
median821
Q31503.75
95-th percentile2160.7
Maximum8773
Range8774
Interquartile range (IQR)1279.75

Descriptive statistics

Standard deviation972.2793982
Coefficient of variation (CV)1.009133492
Kurtosis23.13248928
Mean963.4794661
Median Absolute Deviation (MAD)632.5
Skewness3.450035961
Sum938429
Variance945327.2283
MonotocityNot monotonic
2021-04-09T16:44:50.204675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1230.2%
 
912170.2%
 
2128150.1%
 
984130.1%
 
325100.1%
 
12100.1%
 
513100.1%
 
1918100.1%
 
125190.1%
 
174080.1%
 
145980.1%
 
20380.1%
 
15480.1%
 
91370.1%
 
22870.1%
 
157370.1%
 
94870.1%
 
188270.1%
 
17270.1%
 
3960.1%
 
243360.1%
 
43160.1%
 
22360.1%
 
22660.1%
 
104860.1%
 
Other values (447)7476.8%
 
(Missing)996591.1%
 
ValueCountFrequency (%) 
-12< 0.1%
 
1230.2%
 
23< 0.1%
 
33< 0.1%
 
51< 0.1%
 
74< 0.1%
 
101< 0.1%
 
12100.1%
 
132< 0.1%
 
162< 0.1%
 
ValueCountFrequency (%) 
87731< 0.1%
 
85701< 0.1%
 
84091< 0.1%
 
82451< 0.1%
 
81891< 0.1%
 
81281< 0.1%
 
81271< 0.1%
 
68481< 0.1%
 
24491< 0.1%
 
24472< 0.1%
 

item_id
Categorical

HIGH CARDINALITY
MISSING

Distinct494
Distinct (%)50.7%
Missing9965
Missing (%)91.1%
Memory size45.3 KiB
-Kz10RN3pzqTPrY8Knk1
 
23
-Kz13CMkpzqTPrY8Knk1
 
17
-LbluzPaAs1Wu5WhoO-g
 
15
-Kz13P3opzqTPrY8Knk1
 
13
-L31eQKmUrAqj7a_EFX9
 
10
Other values (489)
896 
ValueCountFrequency (%) 
-Kz10RN3pzqTPrY8Knk1230.2%
 
-Kz13CMkpzqTPrY8Knk1170.2%
 
-LbluzPaAs1Wu5WhoO-g150.1%
 
-Kz13P3opzqTPrY8Knk1130.1%
 
-L31eQKmUrAqj7a_EFX9100.1%
 
-Kz147U1pzqTPrY8Knk190.1%
 
-Kz112hspzqTPrY8Knk180.1%
 
-Kz1-1nupzqTPrY8Knk180.1%
 
-Kz1-uYXpzqTPrY8Knk180.1%
 
-Kz122VxpzqTPrY8Knk170.1%
 
-Kz117W2pzqTPrY8Knk170.1%
 
-Kz13In2pzqTPrY8Knk170.1%
 
-Kz10LYEpzqTPrY8Knk170.1%
 
-Kz1-PQppzqTPrY8Knk170.1%
 
-Kz13CVVpzqTPrY8Knk170.1%
 
-Kz13jhMpzqTPrY8Knk160.1%
 
-Kz1178bpzqTPrY8Knk160.1%
 
-MDJ2uH_FcsJoz2tV1ks60.1%
 
-Kz11eQipzqTPrY8Knk160.1%
 
-Kz10YtfpzqTPrY8Knk160.1%
 
-L31fDdNcQSvxxFIj6-z60.1%
 
-Kz10pk6pzqTPrY8Knk160.1%
 
-L30u9gCZ5uw7pm1C4sR60.1%
 
-Kz11VtWpzqTPrY8Knk15< 0.1%
 
-Kz10TMTpzqTPrY8Knk15< 0.1%
 
Other values (469)7637.0%
 
(Missing)996591.1%
 
2021-04-09T16:44:50.529490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique295 ?
Unique (%)30.3%
2021-04-09T16:44:50.796340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length3
Mean length4.513666697
Min length3

Overview of Unicode Properties

Unique unicode characters64
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2083942.2%
 
a1003020.3%
 
118443.7%
 
z16533.3%
 
K16053.3%
 
-11732.4%
 
P9361.9%
 
Y9041.8%
 
r8831.8%
 
q8661.8%
 
k8661.8%
 
T8531.7%
 
88471.7%
 
p8231.7%
 
03250.7%
 
32680.5%
 
L2330.5%
 
22010.4%
 
u1870.4%
 
l1570.3%
 
W1560.3%
 
C1480.3%
 
51360.3%
 
s1360.3%
 
A1230.2%
 
Other values (39)31836.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3771976.4%
 
Uppercase Letter638812.9%
 
Decimal Number39838.1%
 
Dash Punctuation11732.4%
 
Connector Punctuation1120.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2083955.2%
 
a1003026.6%
 
z16534.4%
 
r8832.3%
 
q8662.3%
 
k8662.3%
 
p8232.2%
 
u1870.5%
 
l1570.4%
 
s1360.4%
 
v1040.3%
 
o980.3%
 
j970.3%
 
i960.3%
 
e930.2%
 
f860.2%
 
w830.2%
 
c800.2%
 
g800.2%
 
t790.2%
 
h780.2%
 
b730.2%
 
x720.2%
 
m600.2%
 
y590.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1173100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
K160525.1%
 
P93614.7%
 
Y90414.2%
 
T85313.4%
 
L2333.6%
 
W1562.4%
 
C1482.3%
 
A1231.9%
 
N1131.8%
 
R1091.7%
 
I1011.6%
 
V1001.6%
 
B971.5%
 
F961.5%
 
M881.4%
 
Q821.3%
 
O801.3%
 
U781.2%
 
S771.2%
 
H741.2%
 
J731.1%
 
X681.1%
 
Z560.9%
 
G530.8%
 
D430.7%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1184446.3%
 
884721.3%
 
03258.2%
 
32686.7%
 
22015.0%
 
51363.4%
 
41193.0%
 
7862.2%
 
6822.1%
 
9751.9%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_112100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4410789.3%
 
Common526810.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2083947.2%
 
a1003022.7%
 
z16533.7%
 
K16053.6%
 
P9362.1%
 
Y9042.0%
 
r8832.0%
 
q8662.0%
 
k8662.0%
 
T8531.9%
 
p8231.9%
 
L2330.5%
 
u1870.4%
 
l1570.4%
 
W1560.4%
 
C1480.3%
 
s1360.3%
 
A1230.3%
 
N1130.3%
 
R1090.2%
 
v1040.2%
 
I1010.2%
 
V1000.2%
 
o980.2%
 
B970.2%
 
Other values (27)19874.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
1184435.0%
 
-117322.3%
 
884716.1%
 
03256.2%
 
32685.1%
 
22013.8%
 
51362.6%
 
41192.3%
 
_1122.1%
 
7861.6%
 
6821.6%
 
9751.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII49375100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2083942.2%
 
a1003020.3%
 
118443.7%
 
z16533.3%
 
K16053.3%
 
-11732.4%
 
P9361.9%
 
Y9041.8%
 
r8831.8%
 
q8661.8%
 
k8661.8%
 
T8531.7%
 
88471.7%
 
p8231.7%
 
03250.7%
 
32680.5%
 
L2330.5%
 
22010.4%
 
u1870.4%
 
l1570.3%
 
W1560.3%
 
C1480.3%
 
51360.3%
 
s1360.3%
 
A1230.2%
 
Other values (39)31836.4%
 

debug_event
Boolean

MISSING

Distinct1
Distinct (%)0.4%
Missing10697
Missing (%)97.8%
Memory size85.6 KiB
1
 
242
(Missing)
10697 
ValueCountFrequency (%) 
12422.2%
 
(Missing)1069797.8%
 
2021-04-09T16:44:50.932824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

previous_os_version
Categorical

MISSING

Distinct14
Distinct (%)24.6%
Missing10882
Missing (%)99.5%
Memory size11.5 KiB
13.7
21 
14.0.1
11 
13.6.1
14.1
14.0
Other values (9)
13 
ValueCountFrequency (%) 
13.7210.2%
 
14.0.1110.1%
 
13.6.160.1%
 
14.13< 0.1%
 
14.03< 0.1%
 
13.62< 0.1%
 
13.3.12< 0.1%
 
13.32< 0.1%
 
102< 0.1%
 
91< 0.1%
 
8.0.01< 0.1%
 
13.5.11< 0.1%
 
12.4.81< 0.1%
 
11.1.21< 0.1%
 
(Missing)1088299.5%
 
2021-04-09T16:44:51.089041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique5 ?
Unique (%)8.8%
2021-04-09T16:44:51.336842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.008684523
Min length1

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2176466.1%
 
a1088233.1%
 
1800.2%
 
.770.2%
 
3380.1%
 
7210.1%
 
0180.1%
 
4180.1%
 
68< 0.1%
 
82< 0.1%
 
22< 0.1%
 
91< 0.1%
 
51< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3264699.2%
 
Decimal Number1890.6%
 
Other Punctuation770.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2176466.7%
 
a1088233.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
18042.3%
 
33820.1%
 
72111.1%
 
0189.5%
 
4189.5%
 
684.2%
 
821.1%
 
221.1%
 
910.5%
 
510.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.77100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3264699.2%
 
Common2660.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2176466.7%
 
a1088233.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
18030.1%
 
.7728.9%
 
33814.3%
 
7217.9%
 
0186.8%
 
4186.8%
 
683.0%
 
820.8%
 
220.8%
 
910.4%
 
510.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII32912100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2176466.1%
 
a1088233.1%
 
1800.2%
 
.770.2%
 
3380.1%
 
7210.1%
 
0180.1%
 
4180.1%
 
68< 0.1%
 
82< 0.1%
 
22< 0.1%
 
91< 0.1%
 
51< 0.1%
 

search_term
Categorical

HIGH CARDINALITY
MISSING

Distinct543
Distinct (%)67.7%
Missing10137
Missing (%)92.7%
Memory size45.7 KiB
912
 
14
984
 
11
381
 
9
1251
 
9
203
 
7
Other values (538)
752 
ValueCountFrequency (%) 
912140.1%
 
984110.1%
 
38190.1%
 
125190.1%
 
20370.1%
 
8260.1%
 
32560.1%
 
3960.1%
 
22860.1%
 
5475< 0.1%
 
8185< 0.1%
 
125< 0.1%
 
dearest lord5< 0.1%
 
2155< 0.1%
 
shepherding in love5< 0.1%
 
2265< 0.1%
 
9134< 0.1%
 
2244< 0.1%
 
844< 0.1%
 
2234< 0.1%
 
4884< 0.1%
 
484< 0.1%
 
jesus lord 4< 0.1%
 
pursue him4< 0.1%
 
once it 4< 0.1%
 
Other values (518)6576.0%
 
(Missing)1013792.7%
 
2021-04-09T16:44:51.631877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique407 ?
Unique (%)50.7%
2021-04-09T16:44:51.952713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length41
Median length3
Mean length3.463387878
Min length1

Overview of Unicode Properties

Unique unicode characters41
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2053754.2%
 
a1044727.6%
 
10862.9%
 
e6771.8%
 
o5131.4%
 
r4081.1%
 
t3901.0%
 
h3811.0%
 
i3721.0%
 
s3320.9%
 
l2780.7%
 
u2140.6%
 
d2050.5%
 
11830.5%
 
21750.5%
 
y1610.4%
 
m1410.4%
 
w1280.3%
 
c1210.3%
 
v1180.3%
 
f1080.3%
 
g1070.3%
 
8900.2%
 
3860.2%
 
4830.2%
 
Other values (16)5451.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3586894.7%
 
Space Separator10862.9%
 
Decimal Number8992.4%
 
Other Punctuation15< 0.1%
 
Final Punctuation14< 0.1%
 
Dash Punctuation4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2053757.3%
 
a1044729.1%
 
e6771.9%
 
o5131.4%
 
r4081.1%
 
t3901.1%
 
h3811.1%
 
i3721.0%
 
s3320.9%
 
l2780.8%
 
u2140.6%
 
d2050.6%
 
y1610.4%
 
m1410.4%
 
w1280.4%
 
c1210.3%
 
v1180.3%
 
f1080.3%
 
g1070.3%
 
p700.2%
 
b660.2%
 
j470.1%
 
k440.1%
 
q2< 0.1%
 
z1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
118320.4%
 
217519.5%
 
89010.0%
 
3869.6%
 
4839.2%
 
9798.8%
 
5778.6%
 
0515.7%
 
7434.8%
 
6323.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1086100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,1173.3%
 
'213.3%
 
!213.3%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
14100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3586894.7%
 
Common20185.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2053757.3%
 
a1044729.1%
 
e6771.9%
 
o5131.4%
 
r4081.1%
 
t3901.1%
 
h3811.1%
 
i3721.0%
 
s3320.9%
 
l2780.8%
 
u2140.6%
 
d2050.6%
 
y1610.4%
 
m1410.4%
 
w1280.4%
 
c1210.3%
 
v1180.3%
 
f1080.3%
 
g1070.3%
 
p700.2%
 
b660.2%
 
j470.1%
 
k440.1%
 
q2< 0.1%
 
z1< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
108653.8%
 
11839.1%
 
21758.7%
 
8904.5%
 
3864.3%
 
4834.1%
 
9793.9%
 
5773.8%
 
0512.5%
 
7432.1%
 
6321.6%
 
140.7%
 
,110.5%
 
-40.2%
 
'20.1%
 
!20.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII37872> 99.9%
 
Punctuation14< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2053754.2%
 
a1044727.6%
 
10862.9%
 
e6771.8%
 
o5131.4%
 
r4081.1%
 
t3901.0%
 
h3811.0%
 
i3721.0%
 
s3320.9%
 
l2780.7%
 
u2140.6%
 
d2050.5%
 
11830.5%
 
21750.5%
 
y1610.4%
 
m1410.4%
 
w1280.3%
 
c1210.3%
 
v1180.3%
 
f1080.3%
 
g1070.3%
 
8900.2%
 
3860.2%
 
4830.2%
 
Other values (15)5311.4%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
14100.0%
 

search_book
Categorical

MISSING

Distinct5
Distinct (%)0.6%
Missing10137
Missing (%)92.7%
Memory size11.0 KiB
All
764 
Hymnal
 
22
New Songs
 
9
History
 
4
Updates
 
3
ValueCountFrequency (%) 
All7647.0%
 
Hymnal220.2%
 
New Songs90.1%
 
History4< 0.1%
 
Updates3< 0.1%
 
(Missing)1013792.7%
 
2021-04-09T16:44:52.257517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:52.451409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:52.700286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length3
Mean length3.013529573
Min length3

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2030561.6%
 
a1016230.8%
 
l15504.7%
 
A7642.3%
 
H260.1%
 
y260.1%
 
m220.1%
 
s16< 0.1%
 
o13< 0.1%
 
e12< 0.1%
 
N9< 0.1%
 
w9< 0.1%
 
9< 0.1%
 
S9< 0.1%
 
g9< 0.1%
 
t7< 0.1%
 
i4< 0.1%
 
r4< 0.1%
 
U3< 0.1%
 
p3< 0.1%
 
d3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3214597.5%
 
Uppercase Letter8112.5%
 
Space Separator9< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2030563.2%
 
a1016231.6%
 
l15504.8%
 
y260.1%
 
m220.1%
 
s16< 0.1%
 
o13< 0.1%
 
e12< 0.1%
 
w9< 0.1%
 
g9< 0.1%
 
t7< 0.1%
 
i4< 0.1%
 
r4< 0.1%
 
p3< 0.1%
 
d3< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A76494.2%
 
H263.2%
 
N91.1%
 
S91.1%
 
U30.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
9100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin32956> 99.9%
 
Common9< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2030561.6%
 
a1016230.8%
 
l15504.7%
 
A7642.3%
 
H260.1%
 
y260.1%
 
m220.1%
 
s16< 0.1%
 
o13< 0.1%
 
e12< 0.1%
 
N9< 0.1%
 
w9< 0.1%
 
S9< 0.1%
 
g9< 0.1%
 
t7< 0.1%
 
i4< 0.1%
 
r4< 0.1%
 
U3< 0.1%
 
p3< 0.1%
 
d3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
9100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII32965100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2030561.6%
 
a1016230.8%
 
l15504.7%
 
A7642.3%
 
H260.1%
 
y260.1%
 
m220.1%
 
s16< 0.1%
 
o13< 0.1%
 
e12< 0.1%
 
N9< 0.1%
 
w9< 0.1%
 
9< 0.1%
 
S9< 0.1%
 
g9< 0.1%
 
t7< 0.1%
 
i4< 0.1%
 
r4< 0.1%
 
U3< 0.1%
 
p3< 0.1%
 
d3< 0.1%
 

search_type
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing10137
Missing (%)92.7%
Memory size10.9 KiB
alphabetical
520 
numerical
282 
ValueCountFrequency (%) 
alphabetical5204.8%
 
numerical2822.6%
 
(Missing)1013792.7%
 
2021-04-09T16:44:52.901241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:53.026210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:53.199598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length3
Mean length3.582502971
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2055652.5%
 
a1197930.6%
 
l13223.4%
 
e8022.0%
 
i8022.0%
 
c8022.0%
 
p5201.3%
 
h5201.3%
 
b5201.3%
 
t5201.3%
 
u2820.7%
 
m2820.7%
 
r2820.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter39189100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2055652.5%
 
a1197930.6%
 
l13223.4%
 
e8022.0%
 
i8022.0%
 
c8022.0%
 
p5201.3%
 
h5201.3%
 
b5201.3%
 
t5201.3%
 
u2820.7%
 
m2820.7%
 
r2820.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin39189100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2055652.5%
 
a1197930.6%
 
l13223.4%
 
e8022.0%
 
i8022.0%
 
c8022.0%
 
p5201.3%
 
h5201.3%
 
b5201.3%
 
t5201.3%
 
u2820.7%
 
m2820.7%
 
r2820.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII39189100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2055652.5%
 
a1197930.6%
 
l13223.4%
 
e8022.0%
 
i8022.0%
 
c8022.0%
 
p5201.3%
 
h5201.3%
 
b5201.3%
 
t5201.3%
 
u2820.7%
 
m2820.7%
 
r2820.7%
 

search_subcategory
Categorical

MISSING

Distinct1
Distinct (%)1.7%
Missing10880
Missing (%)99.5%
Memory size10.9 KiB
All
59 
ValueCountFrequency (%) 
All590.5%
 
(Missing)1088099.5%
 
2021-04-09T16:44:53.419471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-09T16:44:53.558391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:53.692314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2176066.3%
 
a1088033.2%
 
l1180.4%
 
A590.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3275899.8%
 
Uppercase Letter590.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2176066.4%
 
a1088033.2%
 
l1180.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A59100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin32817100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2176066.3%
 
a1088033.2%
 
l1180.4%
 
A590.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII32817100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2176066.3%
 
a1088033.2%
 
l1180.4%
 
A590.2%
 

search_category
Categorical

MISSING

Distinct2
Distinct (%)3.4%
Missing10880
Missing (%)99.5%
Memory size10.9 KiB
All
58 
Encouragement
 
1
ValueCountFrequency (%) 
All580.5%
 
Encouragement1< 0.1%
 
(Missing)1088099.5%
 
2021-04-09T16:44:53.914187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)1.7%
2021-04-09T16:44:54.067099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:54.230030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length3
Mean length3.00091416
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n2176266.3%
 
a1088133.1%
 
l1160.4%
 
A580.2%
 
e2< 0.1%
 
E1< 0.1%
 
c1< 0.1%
 
o1< 0.1%
 
u1< 0.1%
 
r1< 0.1%
 
g1< 0.1%
 
m1< 0.1%
 
t1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3276899.8%
 
Uppercase Letter590.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2176266.4%
 
a1088133.2%
 
l1160.4%
 
e2< 0.1%
 
c1< 0.1%
 
o1< 0.1%
 
u1< 0.1%
 
r1< 0.1%
 
g1< 0.1%
 
m1< 0.1%
 
t1< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A5898.3%
 
E11.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin32827100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2176266.3%
 
a1088133.1%
 
l1160.4%
 
A580.2%
 
e2< 0.1%
 
E1< 0.1%
 
c1< 0.1%
 
o1< 0.1%
 
u1< 0.1%
 
r1< 0.1%
 
g1< 0.1%
 
m1< 0.1%
 
t1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII32827100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n2176266.3%
 
a1088133.1%
 
l1160.4%
 
A580.2%
 
e2< 0.1%
 
E1< 0.1%
 
c1< 0.1%
 
o1< 0.1%
 
u1< 0.1%
 
r1< 0.1%
 
g1< 0.1%
 
m1< 0.1%
 
t1< 0.1%
 

Interactions

2021-04-09T16:43:24.159553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:24.437391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:24.661581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:24.867875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:25.041075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:25.245761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:25.449644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:25.649532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:25.855432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:26.051319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:26.251204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:26.464066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:26.680944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:26.883443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:27.070930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:27.290396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:27.507272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:27.711155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:27.925056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:28.130918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:28.340797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:28.545696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:28.760559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:28.945601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:29.117469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:29.342050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:29.556909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:29.762791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:29.973674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:30.176574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:30.381438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:30.599314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:30.810194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:30.992101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:31.183456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:31.394337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:31.600242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:31.808102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:32.021996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:32.231858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:32.441740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:32.649620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:32.851165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:33.038652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:33.231095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:33.444973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:33.649841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:33.856724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:34.055610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:34.258492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:34.466390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:34.663260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:34.850843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:35.022711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:35.213750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:35.416633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:35.620533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:35.814415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:36.014291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:36.214177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:36.411081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:36.607953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:36.805856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:36.975487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:37.161422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:37.358311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:37.570191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:37.772074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:37.976959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:38.170847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:38.370903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:38.569785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:38.772668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:38.943895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:39.162097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:39.361983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:39.560891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:39.762757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:39.958660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:40.209502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:40.407387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:40.609271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:40.798180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:40.974803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:41.174690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:41.371595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:41.565657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:41.759564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:41.959433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:42.158336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:42.351209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:42.541100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:42.737991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:42.912005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:43.083845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:43.278418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:43.471331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:43.669218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:43.861084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:44.055973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:44.245864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:44.445750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:44.639640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:44.827533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:45.005410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:45.191082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:45.389968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:45.590854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:45.786727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:45.985635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:46.183498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:46.380387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:46.574292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:46.772165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:46.942544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:47.130031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:47.329683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:47.525570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:47.721458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:47.927341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:48.127227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:48.318136image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:48.508027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:48.705918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:48.879738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:49.067222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:49.256353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:49.447262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:49.644132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:49.838021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:50.029914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:50.224819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:50.421688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:50.620575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:50.811466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:50.988751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:51.169094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:51.361965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:51.557856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:51.751965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:51.946830image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:52.141719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:52.343604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:52.537494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:52.741377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:52.926979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:53.098823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:53.287685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:53.480570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:53.677440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:53.876349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:54.073215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:54.266106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:54.461015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:54.658902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:54.855767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:55.020342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:55.208370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:55.405258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:55.599148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:55.792039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:55.989927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:56.179816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:56.375721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:56.569612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:56.760486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:56.941917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:57.113762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:43:57.307968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-09T16:44:54.469308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-09T16:44:55.119634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-09T16:44:55.826927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Missing values

2021-04-09T16:43:58.095518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:11.127031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:15.406872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-09T16:44:17.715335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

event_dateevent_timestampevent_nameevent_previous_timestampevent_bundle_sequence_idevent_server_timestamp_offsetuser_pseudo_iduser_first_touch_timestampstream_idplatformdevice.categorydevice.mobile_brand_namedevice.mobile_model_namedevice.mobile_os_hardware_modeldevice.operating_systemdevice.operating_system_versiondevice.languagedevice.is_limited_ad_trackingdevice.time_zone_offset_secondsgeo.continentgeo.countrygeo.regiongeo.citygeo.sub_continentapp_info.versionapp_info.install_sourcetraffic_source.mediumtraffic_source.sourcedevice.vendor_idindex1first_open_time.valuefirst_open_time.timestampga_session_number.valuega_session_number.timestampga_session_id.valuega_session_id.timestampfirebase_event_origin_xfirebase_event_origin_yga_session_idengaged_session_eventsession_engagedga_session_numberfirebase_screen_idfirebase_screen_classengagement_time_msecentrancesfreeridefirebase_previous_idfirebase_previous_classitem_subcategoryrecommendeditem_categoryitem_nameitem_numberitem_iddebug_eventprevious_os_versionsearch_termsearch_booksearch_typesearch_subcategorysearch_category
01970-01-01 00:00:20.2011072020-11-08 02:15:18.108000session_start2020-11-07 21:47:54.74400013754702cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN015629796000002019-07-13 00:27:23.6278.02020-11-08 02:15:18.1081.604802e+092020-11-08 02:15:18.108autoauto1.604802e+091.01.08.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11970-01-01 00:00:20.2011072020-11-07 21:47:54.744000session_start2020-11-06 16:37:17.31100012718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN115629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.01.07.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21970-01-01 00:00:20.2011072020-11-07 21:47:56.284001screen_view2020-11-06 16:38:57.94100112718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN215629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18MainActivity28592.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31970-01-01 00:00:20.2011072020-11-07 21:48:45.805005user_engagement2020-11-07 21:48:24.89900512718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN315629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18ViewerActivity20888.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
41970-01-01 00:00:20.2011072020-11-07 21:48:45.964006screen_view2020-11-07 21:48:24.92000612718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN415629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18ViewerActivity2468.0NaNNaN5.886279e+18ViewerActivityNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
51970-01-01 00:00:20.2011072020-11-07 21:48:48.433007user_engagement2020-11-07 21:48:45.80500712718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN515629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18ViewerActivity2468.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
61970-01-01 00:00:20.2011072020-11-07 21:48:48.544008screen_view2020-11-07 21:48:45.96400812718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN615629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18ViewerActivity1713.0NaNNaN5.886279e+18ViewerActivityNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
71970-01-01 00:00:20.2011072020-11-07 21:48:50.256009user_engagement2020-11-07 21:48:48.43300912718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN715629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18ViewerActivity1713.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
81970-01-01 00:00:20.2011072020-11-07 21:48:50.314010screen_view2020-11-07 21:48:48.54401012718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN815629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18MainActivityNaNNaNNaN5.886279e+18ViewerActivityNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
91970-01-01 00:00:20.2011072020-11-07 21:48:52.104011user_engagement2020-11-07 21:48:50.25601112718754cb052c8ce7b261aecf783ce043089fb32019-07-13 00:27:23.6271440534155ANDROIDmobileGooglePixelPixelANDROID10en-usNo-21600AmericasUnited StatesTexasAustinNorthern America1.1.7com.android.vendingorganicgoogle-playNaN915629796000002019-07-13 00:27:23.6277.02020-11-07 21:47:54.7441.604786e+092020-11-07 21:47:54.744autoauto1.604786e+091.0NaN7.05.886279e+18MainActivity1835.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

event_dateevent_timestampevent_nameevent_previous_timestampevent_bundle_sequence_idevent_server_timestamp_offsetuser_pseudo_iduser_first_touch_timestampstream_idplatformdevice.categorydevice.mobile_brand_namedevice.mobile_model_namedevice.mobile_os_hardware_modeldevice.operating_systemdevice.operating_system_versiondevice.languagedevice.is_limited_ad_trackingdevice.time_zone_offset_secondsgeo.continentgeo.countrygeo.regiongeo.citygeo.sub_continentapp_info.versionapp_info.install_sourcetraffic_source.mediumtraffic_source.sourcedevice.vendor_idindex1first_open_time.valuefirst_open_time.timestampga_session_number.valuega_session_number.timestampga_session_id.valuega_session_id.timestampfirebase_event_origin_xfirebase_event_origin_yga_session_idengaged_session_eventsession_engagedga_session_numberfirebase_screen_idfirebase_screen_classengagement_time_msecentrancesfreeridefirebase_previous_idfirebase_previous_classitem_subcategoryrecommendeditem_categoryitem_nameitem_numberitem_iddebug_eventprevious_os_versionsearch_termsearch_booksearch_typesearch_subcategorysearch_category
109291970-01-01 00:00:20.2011072020-11-07 15:54:32.262000session_start2020-11-07 03:34:45.7670001716099aa5a4efd33d39f0f197780f39e4c1652020-03-15 04:53:59.6061440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-28800AmericasCanadaBritish ColumbiaBurnabyNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1092915842484000002020-03-15 04:53:59.606121.02020-11-07 15:54:32.2621.604764e+092020-11-07 15:54:32.262autoauto1.604764e+091.01.0121.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109301970-01-01 00:00:20.2011072020-11-07 15:54:32.863001screen_view2020-11-07 03:34:50.4290011716099aa5a4efd33d39f0f197780f39e4c1652020-03-15 04:53:59.6061440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-28800AmericasCanadaBritish ColumbiaBurnabyNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093015842484000002020-03-15 04:53:59.606121.02020-11-07 15:54:32.2621.604764e+092020-11-07 15:54:32.262autoauto1.604764e+091.0NaN121.0-6.969667e+18MainActivity3974.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109311970-01-01 00:00:20.2011072020-11-07 15:54:36.856003user_engagement2020-11-07 03:38:23.7720031716099aa5a4efd33d39f0f197780f39e4c1652020-03-15 04:53:59.6061440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-28800AmericasCanadaBritish ColumbiaBurnabyNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093115842484000002020-03-15 04:53:59.606121.02020-11-07 15:54:32.2621.604764e+092020-11-07 15:54:32.262autoauto1.604764e+091.0NaN121.0-6.969667e+18MainActivity3974.0NaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109321970-01-01 00:00:20.2011072020-11-07 15:54:36.886004screen_view2020-11-07 15:54:32.8630041716099aa5a4efd33d39f0f197780f39e4c1652020-03-15 04:53:59.6061440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-28800AmericasCanadaBritish ColumbiaBurnabyNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093215842484000002020-03-15 04:53:59.606121.02020-11-07 15:54:32.2621.604764e+092020-11-07 15:54:32.262autoauto1.604764e+091.0NaN121.0-6.969667e+18ViewerActivityNaNNaNNaN-6.969667e+18MainActivityNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109331970-01-01 00:00:20.2011072020-11-07 16:54:35.301000session_start2020-11-07 15:54:32.2620001727389aa5a4efd33d39f0f197780f39e4c1652020-03-15 04:53:59.6061440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-28800AmericasCanadaBritish ColumbiaBurnabyNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093315842484000002020-03-15 04:53:59.606122.02020-11-07 16:54:35.3011.604768e+092020-11-07 16:54:35.301autoauto1.604768e+09NaNNaN122.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109341970-01-01 00:00:20.2011072020-11-07 21:10:30.866000session_start2020-11-06 11:03:48.569000554768c52bdfca6431fe0d29deb11bce80d94a2020-01-06 21:57:59.8901440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-18000AmericasCanadaOntarioTorontoNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093415783480000002020-01-06 21:57:59.890359.02020-11-07 21:10:30.8661.604783e+092020-11-07 21:10:30.866autoauto1.604783e+091.01.0359.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109351970-01-01 00:00:20.2011072020-11-07 21:17:57.078004user_engagement2020-11-07 21:16:01.687004554768c52bdfca6431fe0d29deb11bce80d94a2020-01-06 21:57:59.8901440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-18000AmericasCanadaOntarioTorontoNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093515783480000002020-01-06 21:57:59.890359.02020-11-07 21:10:30.8661.604783e+092020-11-07 21:10:30.866autoauto1.604783e+091.0NaN359.0-5.613478e+18MainActivity7592.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109361970-01-01 00:00:20.2011072020-11-07 21:10:31.489001screen_view2020-11-06 11:03:53.104001554768c52bdfca6431fe0d29deb11bce80d94a2020-01-06 21:57:59.8901440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-18000AmericasCanadaOntarioTorontoNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093615783480000002020-01-06 21:57:59.890359.02020-11-07 21:10:30.8661.604783e+092020-11-07 21:10:30.866autoauto1.604783e+091.0NaN359.0-5.613478e+18MainActivityNaN1.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109371970-01-01 00:00:20.2011072020-11-07 21:16:01.687002user_engagement2020-11-06 11:11:36.149002554768c52bdfca6431fe0d29deb11bce80d94a2020-01-06 21:57:59.8901440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-18000AmericasCanadaOntarioTorontoNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093715783480000002020-01-06 21:57:59.890359.02020-11-07 21:10:30.8661.604783e+092020-11-07 21:10:30.866autoauto1.604783e+091.0NaN359.0-5.613478e+18MainActivity330194.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
109381970-01-01 00:00:20.2011072020-11-07 15:54:36.799002view_item2020-11-07 03:34:50.3300021716099aa5a4efd33d39f0f197780f39e4c1652020-03-15 04:53:59.6061440534155ANDROIDmobileOnePlusA6013ONEPLUS A6013ANDROID10en-usNo-28800AmericasCanadaBritish ColumbiaBurnabyNorthern America1.1.7com.android.vendingorganicgoogle-playNaN1093815842484000002020-03-15 04:53:59.606121.02020-11-07 15:54:32.2621.604764e+092020-11-07 15:54:32.262appapp1.604764e+091.0NaN121.0-6.969667e+18MainActivityNaNNaNNaNNaNNaNLife in Eternity0.0Ultimate ManifestationRiver of living water984.0-Kz13P3opzqTPrY8Knk1NaNNaNNaNNaNNaNNaNNaN